Category Alignment Mechanism for Few-Shot Image Classification.

While humans can excel at image classification tasks by comparing a few images, existing metric-based few-shot classification methods are still not well adapted to novel tasks. Performance declines rapidly when encountering new patterns, as feature embeddings cannot effectively encode discriminative properties. Moreover, existing matching methods inadequately utilize support set samples, focusing only on comparing query samples to category prototypes without exploiting contrastive relationships across categories for discriminative features. In this work, we propose a method where query samples select their most category-representative features for matching, making feature embeddings adaptable and category-related. We introduce a category alignment mechanism (CAM) to align query image features with different categories. CAM ensures features chosen for matching are distinct and strongly correlated to intra-and inter-contrastive relationships within categories, making extracted features highly related to their respective categories. CAM is parameter-free, requires no extra training to adapt to new tasks, and adjusts features for matching when task categories change. We also implement a cross-validation-based feature selection technique for support samples, generating more discriminative category prototypes. We implement two versions of inductive and transductive inference and conduct extensive experiments on six datasets to demonstrate the effectiveness of our algorithm. The results indicate that our method consistently yields performance improvements on benchmark tasks and surpasses the current state-of-the-art methods.

One-Step Multi-View Clustering With Diverse Representation.

Multi-View clustering has attracted broad attention due to its capacity to utilize consistent and complementary information among views. Although tremendous progress has been made recently, most existing methods undergo high complexity, preventing them from being applied to large-scale tasks. Multi-View clustering via matrix factorization is a representative to address this issue. However, most of them map the data matrices into a fixed dimension, limiting the model's expressiveness. Moreover, a range of methods suffers from a two-step process, i.e., multimodal learning and the subsequent k -means, inevitably causing a suboptimal clustering result. In light of this, we propose a one-step multi-view clustering with diverse representation (OMVCDR) method, which incorporates multi-view learning and k -means into a unified framework. Specifically, we first project original data matrices into various latent spaces to attain comprehensive information and auto-weight them in a self-supervised manner. Then, we directly use the information matrices under diverse dimensions to obtain consensus discrete clustering labels. The unified work of representation learning and clustering boosts the quality of the final results. Furthermore, we develop an efficient optimization algorithm with proven convergence to solve the resultant problem. Comprehensive experiments on various datasets demonstrate the promising clustering performance of our proposed method. The code is publicly available at https://github.com/wanxinhang/OMVCDR.

On the Consistency and Large-Scale Extension of Multiple Kernel Clustering.

Existing multiple kernel clustering (MKC) algorithms have two ubiquitous problems. From the theoretical perspective, most MKC algorithms lack sufficient theoretical analysis, especially the consistency of learned parameters, such as the kernel weights. From the practical perspective, the high complexity makes MKC unable to handle large-scale datasets. This paper tries to address the above two issues. We first make a consistency analysis of an influential MKC method named Simple Multiple Kernel k-Means (SimpleMKKM). Specifically, suppose that ∧γn are the kernel weights learned by SimpleMKKM from the training samples. We also define the expected version of SimpleMKKM and denote its solution as γ*. We establish an upper bound of ||∧γn-γ*||∞ in the order of ~O(1/√n), where n is the sample number. Based on this result, we also derive its excess clustering risk calculated by a standard clustering loss function. For the large-scale extension, we replace the eigen decomposition of SimpleMKKM with singular value decomposition (SVD). Consequently, the complexity can be decreased to O(n) such that SimpleMKKM can be implemented on large-scale datasets. We then deduce several theoretical results to verify the approximation ability of the proposed SVD-based method. The results of comprehensive experiments demonstrate the superiority of the proposed method. The code is publicly available at https://github.com/weixuan-liang/SVD-based-SimpleMKKM.

Fast Continual Multi-View Clustering With Incomplete Views.

Multi-view clustering (MVC) has attracted broad attention due to its capacity to exploit consistent and complementary information across views. This paper focuses on a challenging issue in MVC called the incomplete continual data problem (ICDP). Specifically, most existing algorithms assume that views are available in advance and overlook the scenarios where data observations of views are accumulated over time. Due to privacy considerations or memory limitations, previous views cannot be stored in these situations. Some works have proposed ways to handle this problem, but all of them fail to address incomplete views. Such an incomplete continual data problem (ICDP) in MVC is difficult to solve since incomplete information with continual data increases the difficulty of extracting consistent and complementary knowledge among views. We propose Fast Continual Multi-View Clustering with Incomplete Views (FCMVC-IV) to address this issue. Specifically, the method maintains a scalable consensus coefficient matrix and updates its knowledge with the incoming incomplete view rather than storing and recomputing all the data matrices. Considering that the given views are incomplete, the newly collected view might contain samples that have yet to appear; two indicator matrices and a rotation matrix are developed to match matrices with different dimensions. In addition, we design a three-step iterative algorithm to solve the resultant problem with linear complexity and proven convergence. Comprehensive experiments conducted on various datasets demonstrate the superiority of FCMVC-IV over the competing approaches. The code is publicly available at https://github.com/wanxinhang/FCMVC-IV.

Subgraph Propagation and Contrastive Calibration for Incomplete Multiview Data Clustering.

The success of multiview raw data mining relies on the integrity of attributes. However, each view faces various noises and collection failures, which leads to a condition that attributes are only partially available. To make matters worse, the attributes in multiview raw data are composed of multiple forms, which makes it more difficult to explore the structure of the data especially in multiview clustering task. Due to the missing data in some views, the clustering task on incomplete multiview data confronts the following challenges, namely: 1) mining the topology of missing data in multiview is an urgent problem to be solved; 2) most approaches do not calibrate the complemented representations with common information of multiple views; and 3) we discover that the cluster distributions obtained from incomplete views have a cluster distribution unaligned problem (CDUP) in the latent space. To solve the above issues, we propose a deep clustering framework based on subgraph propagation and contrastive calibration (SPCC) for incomplete multiview raw data. First, the global structural graph is reconstructed by propagating the subgraphs generated by the complete data of each view. Then, the missing views are completed and calibrated under the guidance of the global structural graph and contrast learning between views. In the latent space, we assume that different views have a common cluster representation in the same dimension. However, in the unsupervised condition, the fact that the cluster distributions of different views do not correspond affects the information completion process to use information from other views. Finally, the complemented cluster distributions for different views are aligned by contrastive learning (CL), thus solving the CDUP in the latent space. Our method achieves advanced performance on six benchmarks, which validates the effectiveness and superiority of our SPCC.

SAMCL: Subgraph-Aligned Multiview Contrastive Learning for Graph Anomaly Detection.

Graph anomaly detection (GAD) has gained increasing attention in various attribute graph applications, i.e., social communication and financial fraud transaction networks. Recently, graph contrastive learning (GCL)-based methods have been widely adopted as the mainstream for GAD with remarkable success. However, existing GCL strategies in GAD mainly focus on node-node and node-subgraph contrast and fail to explore subgraph-subgraph level comparison. Furthermore, the different sizes or component node indices of the sampled subgraph pairs may cause the "nonaligned" issue, making it difficult to accurately measure the similarity of subgraph pairs. In this article, we propose a novel subgraph-aligned multiview contrastive approach for graph anomaly detection, named SAMCL, which fills the subgraph-subgraph contrastive-level blank for GAD tasks. Specifically, we first generate the multiview augmented subgraphs by capturing different neighbors of target nodes forming contrasting subgraph pairs. Then, to fulfill the nonaligned subgraph pair contrast, we propose a subgraph-aligned strategy that estimates similarities with the Earth mover's distance (EMD) of both considering the node embedding distributions and typology awareness. With the newly established similarity measure for subgraphs, we conduct the interview subgraph-aligned contrastive learning module to better detect changes for nodes with different local subgraphs. Moreover, we conduct intraview node-subgraph contrastive learning to supplement richer information on abnormalities. Finally, we also employ the node reconstruction task for the masked subgraph to measure the local change of the target node. Finally, the anomaly score for each node is jointly calculated by these three modules. Extensive experiments conducted on benchmark datasets verify the effectiveness of our approach compared to existing state-of-the-art (SOTA) methods with significant performance gains (up to 6.36% improvement on ACM). Our code can be verified at https://github.com/hujingtao/SAMCL.

Sparse Low-Rank Multi-View Subspace Clustering With Consensus Anchors and Unified Bipartite Graph.

Anchor technology is popularly employed in multi-view subspace clustering (MVSC) to reduce the complexity cost. However, due to the sampling operation being performed on each individual view independently and not considering the distribution of samples in all views, the produced anchors are usually slightly distinguishable, failing to characterize the whole data. Moreover, it is necessary to fuse multiple separated graphs into one, which leads to the final clustering performance heavily subject to the fusion algorithm adopted. What is worse, existing MVSC methods generate dense bipartite graphs, where each sample is associated with all anchor candidates. We argue that this dense-connected mechanism will fail to capture the essential local structures and degrade the discrimination of samples belonging to the respective near anchor clusters. To alleviate these issues, we devise a clustering framework named SL-CAUBG. Specifically, we do not utilize sampling strategy but optimize to generate the consensus anchors within all views so as to explore the information between different views. Based on the consensus anchors, we skip the fusion stage and directly construct the unified bipartite graph across views. Most importantly, l1 norm and Laplacian-rank constraints employed on the unified bipartite graph make it capture both local and global structures simultaneously. l1 norm helps eliminate the scatters between anchors and samples by constructing sparse links and guarantees our graph to be with clear anchor-sample affinity relationship. Laplacian-rank helps extract the global characteristics by measuring the connectivity of unified bipartite graph. To deal with the nondifferentiable objective function caused by l1 norm, we adopt an iterative re-weighted method and the Newton's method. To handle the nonconvex Laplacian-rank, we equivalently transform it as a convex trace constraint. We also devise a four-step alternate method with linear complexity to solve the resultant problem. Substantial experiments show the superiority of our SL-CAUBG.

Graph Clustering with High-Order Contrastive Learning.

Graph clustering is a fundamental and challenging task in unsupervised learning. It has achieved great progress due to contrastive learning. However, we find that there are two problems that need to be addressed: (1) The augmentations in most graph contrastive clustering methods are manual, which can result in semantic drift. (2) Contrastive learning is usually implemented on the feature level, ignoring the structure level, which can lead to sub-optimal performance. In this work, we propose a method termed Graph Clustering with High-Order Contrastive Learning (GCHCL) to solve these problems. First, we construct two views by Laplacian smoothing raw features with different normalizations and design a structure alignment loss to force these two views to be mapped into the same space. Second, we build a contrastive similarity matrix with two structure-based similarity matrices and force it to align with an identity matrix. In this way, our designed contrastive learning encompasses a larger neighborhood, enabling our model to learn clustering-friendly embeddings without the need for an extra clustering module. In addition, our model can be trained on a large dataset. Extensive experiments on five datasets validate the effectiveness of our model. For example, compared to the second-best baselines on four small and medium datasets, our model achieved an average improvement of 3% in accuracy. For the largest dataset, our model achieved an accuracy score of 81.92%, whereas the compared baselines encountered out-of-memory issues.

Improving Embedding Generalization in Few-Shot Learning With Instance Neighbor Constraints.

Recently, metric-based meta-learning methods have been effectively applied to few-shot image classification. These methods classify images based on the relationship between samples in an embedding space, avoiding over-fitting that can occur when training classifiers with limited samples. However, finding an embedding space with good generalization properties remains a challenge. Our work highlights that having an initial manifold space that preserves sample neighbor relationships can prevent the metric model from reaching a suboptimal solution. We propose a feature learning method that leverages Instance Neighbor Constraints (INC). This theory is thoroughly evaluated and analyzed through experiments, demonstrating its effectiveness in improving the efficiency of learning and the overall performance of the model. We further integrate the INC into an alternate optimization training framework (AOT) that leverages both batch learning and episode learning to better optimize the metric-based model. We conduct extensive experiments on 5-way 1-shot and 5-way 5-shot settings on four popular few-shot image benchmarks: miniImageNet, tieredImageNet, Fewshot-CIFAR100 (FC100), and Caltech-UCSD Birds-200-2011(CUB). Results show that our method achieves consistent performance gains on benchmarks and state-of-the-art performance. Our findings suggest that initializing the embedding space appropriately and leveraging both batch and episode learning can significantly improve few-shot learning performance.

Carotid-cavernous fistula following mechanical thrombectomy of the tortuous internal carotid artery: A case report.

BACKGROUND: A carotid-cavernous fistula (CCF) is an abnormal connection between the internal carotid artery (ICA) and the cavernous sinus. Although direct CCFs typically result from trauma or as an iatrogenic complication of neuroendovascular procedures, they can occur as surgery-related complications after mechanical thrombectomy (MT). With the widespread use of MT in patients with acute ischemic stroke complicated with large vessel occlusion, it is important to document CCF following MT and how to avoid them. In this study, we present a case of a patient who developed a CCF following MT and describe in detail the characteristics of ICA tortuosity in this case. CASE SUMMARY: A 60-year-old woman experienced weakness in the left upper and lower limbs as well as difficulty speaking for 4 h. The neurological examination revealed left central facial paralysis and left hemiplegia, with a National Institutes of Health Stroke Scale score of 9. Head magnetic resonance imaging revealed an acute cerebral infarction in the right basal ganglia and radial crown. Magnetic resonance angiography demonstrated an occlusion of the right ICA and middle cerebral artery. Digital subtraction angiography demonstrated distal occlusion of the cervical segment of the right ICA. We performed suction combined with stent thrombectomy. Then, postoperative angiography was performed, which showed a right CCF. One month later, CCF embolization was performed, and the patient's clinical symptoms have significantly improved 5 mo after the operation. CONCLUSION: Although a CCF is a rare complication after MT, it should be considered. Understanding the tortuosity of the internal carotid-cavernous sinus may help predict the complexity of MT and avoid this complication.

Iterative Deep Structural Graph Contrast Clustering for Multiview Raw Data.

Multiview clustering has attracted increasing attention to automatically divide instances into various groups without manual annotations. Traditional shadow methods discover the internal structure of data, while deep multiview clustering (DMVC) utilizes neural networks with clustering-friendly data embeddings. Although both of them achieve impressive performance in practical applications, we find that the former heavily relies on the quality of raw features, while the latter ignores the structure information of data. To address the above issue, we propose a novel method termed iterative deep structural graph contrast clustering (IDSGCC) for multiview raw data consisting of topology learning (TL), representation learning (RL), and graph structure contrastive learning to achieve better performance. The TL module aims to obtain a structured global graph with constraint structural information and then guides the RL to preserve the structural information. In the RL module, graph convolutional network (GCN) takes the global structural graph and raw features as inputs to aggregate the samples of the same cluster and keep the samples of different clusters away. Unlike previous methods performing contrastive learning at the representation level of the samples, in the graph contrastive learning module, we conduct contrastive learning at the graph structure level by imposing a regularization term on the similarity matrix. The credible neighbors of the samples are constructed as positive pairs through the credible graph, and other samples are constructed as negative pairs. The three modules promote each other and finally obtain clustering-friendly embedding. Also, we set up an iterative update mechanism to update the topology to obtain a more credible topology. Impressive clustering results are obtained through the iterative mechanism. Comparative experiments on eight multiview datasets show that our model outperforms the state-of-the-art traditional and deep clustering competitors.

Tri-comb generation with a dual-ring structure.

By introducing a third measurement comb with different repetition frequencies (Δ f r e p ), the tri-comb spectroscopy technique overcomes the ambiguity problem of the original dual-comb spectroscopy technique and eliminates physical delay stages in multidimensional coherent spectroscopy. Nowadays, tri-comb generation based on three frequency-stabilized comb lasers is overly complicated and costly for many potential applications. Previous research on single-cavity dual-combs inspired research on single-cavity tri-combs. However, the currently reported tri-comb structures cannot achieve independently controllable pulses. This paper shows a dual-ring tri-comb seed-source structure using wavelength-based multiplexing in one of the rings. The wavelength and power of the output pulse are independently controlled by using the dual-ring structure. The Δ f r e p of wavelength multiplexing-based dual-comb output can be tuned by adjusting the intra-ring polarization controller (PC). In the case of single-wavelength mode-locking, the PC can be adjusted to achieve a wavelength tuning range of nearly 20 nm. The tri-comb source could offer an attractive alternative solution as a low-complexity light source for field-deployable multi-comb metrology applications.

Task-Related Saliency for Few-Shot Image Classification.

A weakness of the existing metric-based few-shot classification method is that task-unrelated objects or backgrounds may mislead the model since the small number of samples in the support set is insufficient to reveal the task-related targets. An essential cue of human wisdom in the few-shot classification task is that they can recognize the task-related targets by a glimpse of support images without being distracted by task-unrelated things. Thus, we propose to explicitly learn task-related saliency features and make use of them in the metric-based few-shot learning schema. We divide the tackling of the task into three phases, namely, the modeling, the analyzing, and the matching. In the modeling phase, we introduce a saliency sensitive module (SSM), which is an inexact supervision task jointly trained with a standard multiclass classification task. SSM not only enhances the fine-grained representation of feature embedding but also can locate the task-related saliency features. Meanwhile, we propose a self-training-based task-related saliency network (TRSN) which is a lightweight network to distill task-related salience produced by SSM. In the analyzing phase, we freeze TRSN and use it to handle novel tasks. TRSN extracts task-relevant features while suppressing the disturbing task-unrelated features. We, therefore, can discriminate samples accurately in the matching phase by strengthening the task-related features. We conduct extensive experiments on five-way 1-shot and 5-shot settings to evaluate the proposed method. Results show that our method achieves a consistent performance gain on benchmarks and achieves the state-of-the-art.

A single-cell atlas identifies pretreatment features of primary imatinib resistance in chronic myeloid leukemia.

Primary resistance to tyrosine kinase inhibitors (TKIs) is a significant barrier to optimal outcomes in chronic myeloid leukemia (CML), but factors contributing to response heterogeneity remain unclear. Using single-cell RNA (scRNA) sequencing, we identified 8 statistically significant features in pretreatment bone marrow, which correlated with either sensitivity (major molecular response or MMR) or extreme resistance to imatinib (eventual blast crisis [BC] transformation). Employing machine-learning, we identified leukemic stem cell (LSC) and natural killer (NK) cell gene expression profiles predicting imatinib response with >80% accuracy, including no false positives for predicting BC. A canonical erythroid-specifying (TAL1/KLF1/GATA1) regulon was a hallmark of LSCs from patients with MMR and was associated with erythroid progenitor [ERP] expansion in vivo (P < .05), and a 2- to 10-fold (6.3-fold in group A vs 1.09-fold in group C) erythroid over myeloid bias in vitro. Notably, ERPs demonstrated exquisite TKI sensitivity compared with myeloid progenitors (P < .001). These LSC features were lost with progressive resistance, and MYC- and IRF1-driven inflammatory regulons were evident in patients who progressed to transformation. Patients with MMR also exhibited a 56-fold expansion (P < .01) of a normally rare subset of hyperfunctional adaptive-like NK cells, which diminished with progressive resistance, whereas patients destined for BC accumulated inhibitory NKG2A+ NK cells favoring NK cell tolerance. Finally, we developed antibody panels to validate our scRNA-seq findings. These panels may be useful for prospective studies of primary resistance, and in assessing the contribution of predetermined vs acquired factors in TKI response heterogeneity.

Late Fusion Multiple Kernel Clustering With Proxy Graph Refinement.

Multiple kernel clustering (MKC) optimally utilizes a group of pre-specified base kernels to improve clustering performance. Among existing MKC algorithms, the recently proposed late fusion MKC methods demonstrate promising clustering performance in various applications and enjoy considerable computational acceleration. However, we observe that the kernel partition learning and late fusion processes are separated from each other in the existing mechanism, which may lead to suboptimal solutions and adversely affect the clustering performance. In this article, we propose a novel late fusion multiple kernel clustering with proxy graph refinement (LFMKC-PGR) framework to address these issues. First, we theoretically revisit the connection between late fusion kernel base partition and traditional spectral embedding. Based on this observation, we construct a proxy self-expressive graph from kernel base partitions. The proxy graph in return refines the individual kernel partitions and also captures partition relations in graph structure rather than simple linear transformation. We also provide theoretical connections and considerations between the proposed framework and the multiple kernel subspace clustering. An alternate algorithm with proved convergence is then developed to solve the resultant optimization problem. After that, extensive experiments are conducted on 12 multi-kernel benchmark datasets, and the results demonstrate the effectiveness of our proposed algorithm. The code of the proposed algorithm is publicly available at https://github.com/wangsiwei2010/graphlatefusion_MKC.

Multiple Kernel Clustering With Compressed Subspace Alignment.

Multiple kernel clustering (MKC) has recently achieved remarkable progress in fusing multisource information to boost the clustering performance. However, the O(n2) memory consumption and O(n3) computational complexity prohibit these methods from being applied into median- or large-scale applications, where n denotes the number of samples. To address these issues, we carefully redesign the formulation of subspace segmentation-based MKC, which reduces the memory and computational complexity to O(n) and O(n2) , respectively. The proposed algorithm adopts a novel sampling strategy to enhance the performance and accelerate the speed of MKC. Specifically, we first mathematically model the sampling process and then learn it simultaneously during the procedure of information fusion. By this way, the generated anchor point set can better serve data reconstruction across different views, leading to improved discriminative capability of the reconstruction matrix and boosted clustering performance. Although the integrated sampling process makes the proposed algorithm less efficient than the linear complexity algorithms, the elaborate formulation makes our algorithm straightforward for parallelization. Through the acceleration of GPU and multicore techniques, our algorithm achieves superior performance against the compared state-of-the-art methods on six datasets with comparable time cost to the linear complexity algorithms.

E 3Outlier: a Self-Supervised Framework for Unsupervised Deep Outlier Detection.

Existing unsupervised outlier detection (OD) solutions face a grave challenge with surging visual data like images. Although deep neural networks (DNNs) prove successful for visual data, deep OD remains difficult due to OD's unsupervised nature. This paper proposes a novel framework named E 3Outlier that can perform effective and end-to-end deep outlier removal. Its core idea is to introduce self-supervision into deep OD. Specifically, our major solution is to adopt a discriminative learning paradigm that creates multiple pseudo classes from given unlabeled data by various data operations, which enables us to apply prevalent discriminative DNNs (e.g., ResNet) to the unsupervised OD problem. Then, with theoretical and empirical demonstration, we argue that inlier priority, a property that encourages DNN to prioritize inliers during self-supervised learning, makes it possible to perform end-to-end OD. Meanwhile, unlike frequently-used outlierness measures (e.g., density, proximity) in previous OD methods, we explore network uncertainty and validate it as a highly effective outlierness measure, while two practical score refinement strategies are also designed to improve OD performance. Finally, in addition to the discriminative learning paradigm above, we also explore the solutions that exploit other learning paradigms (i.e., generative learning and contrastive learning) to introduce self-supervision for E 3Outlier. Such extendibility not only brings further performance gain on relatively difficult datasets, but also enables E 3Outlier to be applied to other OD applications like video abnormal event detection. Extensive experiments demonstrate that E 3Outlier can considerably outperform state-of-the-art counterparts by 10%-30% AUROC. Demo codes are available at https://github.com/demonzyj56/E3Outlier.

Semantic instance segmentation with discriminative deep supervision for medical images.

Semantic instance segmentation is crucial for many medical image analysis applications, including computational pathology and automated radiation therapy. Existing methods for this task can be roughly classified into two categories: (1) proposal-based methods and (2) proposal-free methods. However, in medical images, the irregular shape-variations and crowding instances (e.g., nuclei and cells) make it hard for the proposal-based methods to achieve robust instance localization. On the other hand, ambiguous boundaries caused by the low-contrast nature of medical images (e.g., CT images) challenge the accuracy of the proposal-free methods. To tackle these issues, we propose a proposal-free segmentation network with discriminative deep supervision (DDS), which at the same time allows us to gain the power of the proposal-based method. The DDS module is interleaved with a carefully designed proposal-free segmentation backbone in our network. Consequently, the features learned by the backbone network become more sensitive to instance localization. Also, with the proposed DDS module, robust pixel-wise instance-level cues (especially structural information) are introduced for semantic segmentation. Extensive experiments on three datasets, i.e., a nuclei dataset, a pelvic CT image dataset, and a synthetic dataset, demonstrate the superior performance of the proposed algorithm compared to the previous works.

Interpolation-Based Contrastive Learning for Few-Label Semi-Supervised Learning.

Semi-supervised learning (SSL) has long been proved to be an effective technique to construct powerful models with limited labels. In the existing literature, consistency regularization-based methods, which force the perturbed samples to have similar predictions with the original ones have attracted much attention for their promising accuracy. However, we observe that the performance of such methods decreases drastically when the labels get extremely limited, e.g., 2 or 3 labels for each category. Our empirical study finds that the main problem lies with the drift of semantic information in the procedure of data augmentation. The problem can be alleviated when enough supervision is provided. However, when little guidance is available, the incorrect regularization would mislead the network and undermine the performance of the algorithm. To tackle the problem, we: 1) propose an interpolation-based method to construct more reliable positive sample pairs and 2) design a novel contrastive loss to guide the embedding of the learned network to change linearly between samples so as to improve the discriminative capability of the network by enlarging the margin decision boundaries. Since no destructive regularization is introduced, the performance of our proposed algorithm is largely improved. Specifically, the proposed algorithm outperforms the second best algorithm (Comatch) with 5.3% by achieving 88.73% classification accuracy when only two labels are available for each class on the CIFAR-10 dataset. Moreover, we further prove the generality of the proposed method by improving the performance of the existing state-of-the-art algorithms considerably with our proposed strategy. The corresponding code is available at https://github.com/xihongyang1999/ICL_SSL.

Local Sample-Weighted Multiple Kernel Clustering With Consensus Discriminative Graph.

Multiple kernel clustering (MKC) is committed to achieving optimal information fusion from a set of base kernels. Constructing precise and local kernel matrices is proven to be of vital significance in applications since the unreliable distant-distance similarity estimation would degrade clustering performance. Although existing localized MKC algorithms exhibit improved performance compared with globally designed competitors, most of them widely adopt the KNN mechanism to localize kernel matrix by accounting for τ -nearest neighbors. However, such a coarse manner follows an unreasonable strategy that the ranking importance of different neighbors is equal, which is impractical in applications. To alleviate such problems, this article proposes a novel local sample-weighted MKC (LSWMKC) model. We first construct a consensus discriminative affinity graph in kernel space, revealing the latent local structures. Furthermore, an optimal neighborhood kernel for the learned affinity graph is output with naturally sparse property and clear block diagonal structure. Moreover, LSWMKC implicitly optimizes adaptive weights on different neighbors with corresponding samples. Experimental results demonstrate that our LSWMKC possesses better local manifold representation and outperforms existing kernel or graph-based clustering algorithms. The source code of LSWMKC can be publicly accessed from https://github.com/liliangnudt/LSWMKC.