Causal Disentanglement Domain Generalization for time-series signal fault diagnosis.

Domain generalization-based fault diagnosis (DGFD) presents significant prospects for recognizing faults without the accessibility of the target domain. Previous DGFD methods have achieved significant progress; however, there are some limitations. First, most DGFG methods statistically model the dependence between time-series data and labels, and they are superficial descriptions to the actual data-generating process. Second, most of the existing DGFD methods are only verified on vibrational time-series datasets, which is insufficient to show the potential of domain generalization in the fault diagnosis area. In response to the above issues, this paper first proposes a DGFD method named Causal Disentanglement Domain Generalization (CDDG), which can reestablish the data-generating process by disentangling time-series data into the causal factors (fault-related representation) and no-casual factors (domain-related representation) with a structural causal model. Specifically, in CDDG, causal aggregation loss is designed to separate the unobservable causal and non-causal factors. Meanwhile, the reconstruction loss is proposed to ensure the information completeness of the disentangled factors. We also introduce a redundancy reduction loss to learn efficient features. The proposed CDDG is verified on five cross-machine vibrational fault diagnosis cases and three cross-environment acoustical anomaly detection cases by comparing it with eight state-of-the-art (SOTA) DGFD methods. We publicize the open-source time-series DGFD Benchmark containing CDDG and the eight SOTA methods. The code repository will be available at https://github.com/ShaneSpace/DGFDBenchmark.

Modeling Sequential Annotations for Sequence Labeling With Crowds.

Crowd sequential annotations can be an efficient and cost-effective way to build large datasets for sequence labeling. Different from tagging independent instances, for crowd sequential annotations, the quality of label sequence relies on the expertise level of annotators in capturing internal dependencies for each token in the sequence. In this article, we propose modeling sequential annotation for sequence labeling with crowds (SA-SLC). First, a conditional probabilistic model is developed to jointly model sequential data and annotators' expertise, in which categorical distribution is introduced to estimate the reliability of each annotator in capturing local and nonlocal label dependencies for sequential annotation. To accelerate the marginalization of the proposed model, a valid label sequence inference (VLSE) method is proposed to derive the valid ground-truth label sequences from crowd sequential annotations. VLSE derives possible ground-truth labels from the tokenwise level and further prunes subpaths in the forward inference for label sequence decoding. VLSE reduces the number of candidate label sequences and improves the quality of possible ground-truth label sequences. The experimental results on several sequence labeling tasks of Natural Language Processing show the effectiveness of the proposed model.

Modeling Self-Representation Label Correlations for Textual Aspects and Emojis Recommendation.

The rapid development of Internet services and social platforms encourages users to share their opinions. To help users give valuable comments, content providers expect the recommender system to offer appropriate suggestions, including specific features of the item described in texts and emojis, which are all considered aspects of the user reviews. Hence, the review aspect recommendation task has become significant, where the key lies in handling personal preferences and semantic correlations between suggested items. This article proposes a correlation-aware review aspect recommender (CARAR) system model by constructing self-representation correlations between different views of review aspects, including textual aspects and emojis to make a personalized recommendation. The dependencies between different textual aspects and emojis can be identified and utilized to facilitate the factorization process to learn user and item latent factors. The cross-view correlation mapping between textual aspects and emojis can be built to enhance the recommendation performance. Moreover, the additional information in the real-world environment is also applied to our model to adjust the recommendation results. We constructed experiments on five self-collected and public datasets and compared with six existing models. The results show that our model can outperform the existing models on review aspects recommendation tasks, validating the effectiveness of our approach.

ExpGCN: Review-aware Graph Convolution Network for explainable recommendation.

Existing works in recommender system have widely explored extracting reviews as explanations beyond user-item interactions, and formulated the explanation generation as a ranking task to enhance item recommendation performance. To associate explanations with users and items, graph neural networks (GNN) are usually employed to learn node representations on the heterogeneous user-item-explanation interaction graph. However, modeling heterogeneous graph convolution poses limitations in both message passing styles and computational efficiency, resulting in sub-optimal recommendation performance. To address the limitations, we propose an Explanation-aware Graph Convolution Network (ExpGCN). In particular, the heterogeneous interaction graph is divided to subgraphs regard to the edge types in ExpGCN. By aggregating information from distinct subgraphs, ExpGCN is capable of generating node representations for explanation ranking task and item recommendation task respectively. Task-oriented graph convolution can not only reduce the complexity of heterogeneous node aggregation, but also alleviate the performance degeneration caused by the conflicts between task learning objectives, which has been neglected in current studies. Extensive experiments on four public datasets show that ExpGCN significantly outperforms state-of-the-art baselines with high efficiency, demonstrating the effectiveness of ExpGCN in explainable recommendations.

Partial Sequence Labeling With Structured Gaussian Processes.

Existing partial sequence labeling models mainly focus on a max-margin framework that fails to provide an uncertainty estimation of the prediction. Furthermore, the unique ground-truth disambiguation strategy employed by these models may include wrong label information for parameter learning. In this article, we propose structured Gaussian processes for partial sequence labeling (SGPPSL), which encodes uncertainty in the prediction and does not need extra effort for model selection and hyperparameter learning. The model employs factor-as-piece approximation that divides the linear-chain graph structure into the set of pieces, which preserves the basic Markov random field structure and effectively avoids handling a large number of candidate output sequences generated by partially annotated data. Then, confidence measure is introduced in the model to address different contributions of candidate labels, which enables the ground-truth label information to be utilized in parameter learning. Based on the derived lower bound of the variational lower bound of the proposed model, variational parameters and confidence measures are estimated in the framework of alternating optimization. Moreover, a weighted Viterbi algorithm is proposed to incorporate confidence measures to sequence prediction, which considers label ambiguity arose from multiple annotations in the training data and thus helps improve the performance. SGPPSL is evaluated on several sequence labeling tasks and the experimental results show the effectiveness of the proposed model.

Semantic-Gap-Oriented Feature Selection and Classifier Construction in Multilabel Learning.

Multilabel learning focuses on assigning instances with different labels. In essence, the multilabel learning aims at learning a predictive function from feature space to a label space. The predictive function learning procedure can be regarded as a feature selection procedure and as a classifier construction procedure. For feature selection, we extract features for each label based on the learned positive and negative feature-label correlations. The positive and negative relationships can illustrate which labels can and cannot be well presented by the corresponding features, respectively, due to the semantic gap. For classifier construction, we perform sample-specific and label-specific classifications. The interlabel and interinstance correlations are combined in these two kinds of classifications. These two correlations are learned from both input features and output labels when the output labels are too sparse to reveal the informative correlation. However, there exists the semantic gap when combining input and output spaces to mine the labelwise relationship. The semantic gap can be bridged by the learned feature-label correlation. Finally, extensive experimental results on several benchmarks under four domains are presented to show the effectiveness of the proposed framework.

Multilabel Classification With Group-Based Mapping: A Framework With Local Feature Selection and Local Label Correlation.

Multilabel learning, which handles instances associated with multiple labels, has attracted much attention in recent years. Many extant multilabel feature selection methods target global feature selection, which means feature selection weights for each label are shared by all instances. Also, many extant multilabel classification methods exploit global label selection, which means labels correlations are shared by all instances. In real-world objects, however, different subsets of instances may share different feature selection weights and different label correlations. In this article, we propose a novel framework with local feature selection and local label correlation, where we assume instances can be clustered into different groups, and the feature selection weights and label correlations can only be shared by instances in the same group. The proposed framework includes a group-specific feature selection process and a label-specific group selection process. The former process projects instances into different groups by extracting the instance-group correlation. The latter process selects labels for each instance based on its related groups by extracting the group-label correlation. In addition, we also exploit the intergroup correlation. These three kinds of group-based correlations are combined to perform effective multilabel classification. The experimental results on various datasets validate the effectiveness of our approach.

Topic-Based Instance and Feature Selection in Multilabel Classification.

Multilabel learning has been extensively studied in the past years, as it has many applications in different domains. It aims at annotating the labels for unseen data according to training data, which are often high dimensional in both instance and feature levels. The training data often have noisy and redundant information on these two levels. As an effective data preprocessing step, instance and feature selection should both be performed to find relevant training instances for each testing instance and relevant features for each label, respectively. However, most of the existing methods overlook the input-output correlation in each kind of selection. It will lead to the performance degradation. This article presents a formulation for multilabel learning from a topic view that exploits the dependence between features and labels in a topic space. We can perform effective instance and feature selection in the latent topic space, as the relationship between the input and output spaces is well captured in this space. The results from intensive experiments on various benchmarks demonstrate the effectiveness of the proposed framework.

Weak Disambiguation for Partial Structured Output Learning.

Existing disambiguation strategies for partial structured output learning just cannot generalize well to solve the problem that there are some candidates that can be false positive or similar to the ground-truth label. In this article, we propose a novel weak disambiguation for partial structured output learning (WD-PSL). First, a piecewise large margin formulation is generalized to partial structured output learning, which effectively avoids handling a large number of candidate-structured outputs for complex structures. Second, in the proposed weak disambiguation strategy, each candidate label is assigned with a confidence value indicating how likely it is the true label, which aims to reduce the negative effects of wrong ground-truth label assignment in the learning process. Then, two large margins are formulated to combine two types of constraints which are the disambiguation between candidates and noncandidates, and the weak disambiguation for candidates. In the framework of alternating optimization, a new 2n -slack variables cutting plane algorithm is developed to accelerate each iteration of optimization. The experimental results on several sequence labeling tasks of natural language processing show the effectiveness of the proposed model.

Multilabel Classification With Label-Specific Features and Classifiers: A Coarse- and Fine-Tuned Framework.

Multilabel classification deals with instances assigned with multiple labels simultaneously. It focuses on learning a mapping from feature space to label a space for out-of-sample extrapolation. The mapping can be seen as a feature selection process in the feature domain or as a classifier training process in the classifier domain. The existing methods do not effectively learn the mapping when combining these two domains together. In this article, we derive a mechanism to extract label-specific features in local and global levels. We also derive a mechanism to train label-specific classifiers in individual and joint levels. Extracting features globally and training classifiers jointly can be seen as a dual process of learning the mapping function on two domains in a coarse-tuned way, while extracting features locally and training classifiers individually can be seen as a dual process of learning the mapping function on two domains in a fine-tuned way. The two-level feature selection and the two-level classifier training are derived to make the entire mapping learning process robust. Finally, extensive experimental results on several benchmarks under four domains are presented to demonstrate the effectiveness of the proposed approach.

Spatiotemporal Tree Filtering for Enhancing Image Change Detection.

Change detection has received extensive attention because of its realistic significance and broad application fields. However, none of the existing change detection algorithms can handle all scenarios and tasks so far. Different from the most of contributions from the research community in recent years, this paper does not work on designing new change detection algorithms. We, instead, solve the problem from another perspective by enhancing the raw detection results after change detection. As a result, the proposed method is applicable to various kinds of change detection methods, and regardless of how the results are detected. In this paper, we propose Fast Spatiotemporal Tree Filter (FSTF), a purely unsupervised detection method, to enhance coarse binary detection masks obtained by different kinds of change detection methods. In detail, the proposed FSTF has adopted a volumetric structure to effectively synthesize spatiotemporal information of the same target from the current time and history frames to enhance detection. The computational complexity analyzed in the view of graph theory also show that the fast realization of FSTF is a linear time algorithm, which is capable of handling efficient on-line detection tasks. Finally, comprehensive experiments based on qualitative and quantitative analysis verify that FSTF-based change detection enhancement is superior to several other state-of-the-art methods including fully connected Conditional Random Field (CRF), joint bilateral filter, and guided filter. It is illustrated that FSTF is versatile enough to also improve saliency detection as well as semantic image segmentation.

Three-dimensional ultrasound evaluation of the effects of pomegranate therapy on carotid plaque texture using locality preserving projection.

BACKGROUND AND OBJECTIVE: Dietary supplements are expected to confer a smaller beneficial effect than medical treatments. Therefore, there is a need to develop cost-effective biomarkers that can demonstrate the efficacy of such supplements for carotid atherosclerosis. The aim of this study is to develop such a biomarker based on the changes of 376 plaque textural features measured from 3D ultrasound images. METHODS: Since the number of features (376) was greater than the number of subjects (171) in this study, principal component analysis (PCA) was applied to reduce the dimensionality of feature vectors. To generate a scalar biomarker for each subject, elements in the reduced feature vectors produced by PCA were weighted using locality preserving projections (LPP) to capture essential patterns exhibited locally in the feature space. 96 subjects treated by pomegranate juice and tablets, and 75 subjects receiving placebo-matching juice and tablets were evaluated in this study. The discriminative power of the proposed biomarker was evaluated and compared with existing biomarkers using t-tests. As the cost of a clinical trial is directly related to the number of subjects enrolled, the cost-effectiveness of the proposed biomarker was evaluated by sample size estimation. RESULTS: The proposed biomarker was more able to discriminate plaque changes exhibited by the pomegranate and placebo groups than total plaque volume (TPV) according to the result of t-tests (TPV: p=0.34, Proposed biomarker: p=1.5×10-5). The sample size required by the new biomarker to detect a significant effect was 20 times smaller than that required by TPV. CONCLUSION: With the increase in cost-effectiveness afforded by the proposed biomarker, more proof-of-principle studies for novel treatment options could be performed.

Bridging User Interest to Item Content for Recommender Systems: An Optimization Model.

Recommender systems are currently utilized widely in e-commerce for product recommendations and within content delivery platforms. Previous studies usually use independent features to represent item content. As a result, the relationship hidden among the content features is overlooked. In fact, the reason that an item attracts a user may be attributed to only a few set of features. In addition, these features are often semantically coupled. In this paper, we present an optimization model for extracting the relationship hidden in content features by considering user preferences. The learned feature relationship matrix is then applied to address the cold-start recommendations and content-based recommendations. It could also easily be employed for the visualization of feature relation graphs. Our proposed method was examined on three public datasets: 1) hetrec-movielens-2k-v2; 2) book-crossing; and 3) Netflix. The experimental results demonstrated the effectiveness of our method in comparison to the state-of-the-art recommendation methods.

Exactly Robust Kernel Principal Component Analysis.

Robust principal component analysis (RPCA) can recover low-rank matrices when they are corrupted by sparse noises. In practice, many matrices are, however, of high rank and, hence, cannot be recovered by RPCA. We propose a novel method called robust kernel principal component analysis (RKPCA) to decompose a partially corrupted matrix as a sparse matrix plus a high- or full-rank matrix with low latent dimensionality. RKPCA can be applied to many problems such as noise removal and subspace clustering and is still the only unsupervised nonlinear method robust to sparse noises. Our theoretical analysis shows that, with high probability, RKPCA can provide high recovery accuracy. The optimization of RKPCA involves nonconvex and indifferentiable problems. We propose two nonconvex optimization algorithms for RKPCA. They are alternating direction method of multipliers with backtracking line search and proximal linearized minimization with adaptive step size (AdSS). Comparative studies in noise removal and robust subspace clustering corroborate the effectiveness and the superiority of RKPCA.

Label-specific feature selection and two-level label recovery for multi-label classification with missing labels.

In multi-label learning, each instance is assigned by several nonexclusive labels. However, these labels are often incomplete, resulting in unsatisfactory performance in label related applications. We design a two-level label recovery mechanism to perform label imputation in training sets. An instance-wise semantic relational graph and a label-wise semantic relational graph are used in this mechanism to recover the label matrix. These two graphs exhibit a capability of capturing reliable two-level semantic correlations. We also design a label-specific feature selection mechanism to perform label prediction in testing sets. The local and global feature-label connection are both exploited in this mechanism to learn an inductive classifier. By updating the matrix that represents the relevance between features and the predicted labels, the label-specific feature selection mechanism is robust to missing labels. At last, intensive experimental results on nine datasets under different domains are presented to demonstrate the effectiveness of the proposed approach.

Topic-Based Algorithm for Multilabel Learning With Missing Labels.

In multilabel learning (MLL), each instance can be assigned by several concepts simultaneously from a class dictionary. Usually, labels in the class dictionary have semantic correlations and semantic hierarchy. Instances can be categorized into different topics. Each topic has its own label candidates, and some topics have overlapped label candidates. In this paper, we propose a novel MLL method to deal with missing labels. The proposed algorithm can recover the label matrix according to local, topic-wise, and global semantic properties. Specifically, in the global level, label consistency, label-wise semantic correlations, and semantic hierarchy are exploited; in the local level, label importance and instance-wise semantic correlations in each topic are extracted; and in the topic level, label importance similarities and instance-wise semantic similarities between topics are mined. The experimental results on five image data sets in different applications demonstrate the effectiveness of the proposed approach.

Prostate lesion delineation from multiparametric magnetic resonance imaging based on locality alignment discriminant analysis.

PURPOSE: Multiparametric MRI (mpMRI) has shown promise in the detection and localization of prostate cancer foci. Although techniques have been previously introduced to delineate lesions from mpMRI, these techniques were evaluated in datasets with T2 maps available. The generation of T2 map is not included in the clinical prostate mpMRI consensus guidelines; the acquisition of which requires repeated T2-weighted (T2W) scans and would significantly lengthen the scan time currently required for the clinically recommended acquisition protocol, which includes T2W, diffusion-weighted (DW), and dynamic contrast-enhanced (DCE) imaging. The goal of this study is to develop and evaluate an algorithm that provides pixel-accurate lesion delineation from images acquired based on the clinical protocol. METHODS: Twenty-five pixel-based features were extracted from the T2-weighted (T2W), apparent diffusion coefficient (ADC), and dynamic contrast-enhanced (DCE) images. The pixel-wise classification was performed on the reduced space generated by locality alignment discriminant analysis (LADA), a version of linear discriminant analysis (LDA) localized to patches in the feature space. Postprocessing procedures, including the removal of isolated points identified and filling of holes inside detected regions, were performed to improve delineation accuracy. The segmentation result was evaluated against the lesions manually delineated by four expert observers according to the Prostate Imaging-Reporting and Data System (PI-RADS) detection guideline. RESULTS: The LADA-based classifier (60 ± 11%) achieved a higher sensitivity than the LDA-based classifier (51 ± 10%), thereby demonstrating, for the first time, that higher classification performance was attained on the reduced space generated by LADA than by LDA. Further sensitivity improvement (75 ± 14%) was obtained after postprocessing, approaching the sensitivities attained by previous mpMRI lesion delineation studies in which nonclinical T2 maps were available. CONCLUSION: The proposed algorithm delineated lesions accurately and efficiently from images acquired following the clinical protocol. The development of this framework may potentially accelerate the clinical uses of mpMRI in prostate cancer diagnosis and treatment planning.

Tree2Vector: Learning a Vectorial Representation for Tree-Structured Data.

The tree structure is one of the most powerful structures for data organization. An efficient learning framework for transforming tree-structured data into vectorial representations is presented. First, in attempting to uncover the global discriminative information of child nodes hidden at the same level of all of the trees, a clustering technique can be adopted for allocating children into different clusters, which are used to formulate the components of a vector. Moreover, a locality-sensitive reconstruction method is introduced to model a process, in which each parent node is assumed to be reconstructed by its children. The resulting reconstruction coefficients are reversely transformed into complementary coefficients, which are utilized for locally weighting the components of the vector. A new vector is formulated by concatenating the original parent node vector and the learned vector from its children. This new vector for each parent node is inputted into the learning process of formulating vectorial representation at the upper level of the tree. This recursive process concludes when a vectorial representation is achieved for the entire tree. Our method is examined in two applications: book author recommendations and content-based image retrieval. Extensive experimental results demonstrate the effectiveness of the proposed method for transforming tree-structured data into vectors.

Accelerated low-rank representation for subspace clustering and semi-supervised classification on large-scale data.

The scalability of low-rank representation (LRR) to large-scale data is still a major research issue, because it is extremely time-consuming to solve singular value decomposition (SVD) in each optimization iteration especially for large matrices. Several methods were proposed to speed up LRR, but they are still computationally heavy, and the overall representation results were also found degenerated. In this paper, a novel method, called accelerated LRR (ALRR) is proposed for large-scale data. The proposed accelerated method integrates matrix factorization with nuclear-norm minimization to find a low-rank representation. In our proposed method, the large square matrix of representation coefficients is transformed into a significantly smaller square matrix, on which SVD can be efficiently implemented. The size of the transformed matrix is not related to the number of data points and the optimization of ALRR is linear with the number of data points. The proposed ALRR is convex, accurate, robust, and efficient for large-scale data. In this paper, ALRR is compared with state-of-the-art in subspace clustering and semi-supervised classification on real image datasets. The obtained results verify the effectiveness and superiority of the proposed ALRR method.

Sensitive three-dimensional ultrasound assessment of carotid atherosclerosis by weighted average of local vessel wall and plaque thickness change.

PURPOSE: Vitamin B deficiency has been identified as a risk factor for vascular events. However, the reduction of vascular events was not shown in large randomized controlled trials evaluating B-Vitamin therapy. There is an important requirement to develop sensitive biomarkers to be used as efficacy targets for B-Vitamin therapy as well as other dietary treatments and lifestyle regimes that are being developed. Carotid vessel-wall-plus-plaque thickness change (VWT-Change) measured from 3D ultrasound has been shown to be sensitive to atorvastatin therapies in previous studies. However, B-Vitamin treatment is expected to confer a smaller beneficial effect in carotid atherosclerosis than the strong dose of atorvastatin. This paper introduces a sensitive atherosclerosis biomarker based on the weighted mean VWT-Change measurement from 3D ultrasound with a purpose to detect statistically significant effect of B-Vitamin therapy. METHODS: Of the 56 subjects analyzed in this study, 27 were randomized to receive a B-Vitamin tablet daily and 29 received a placebo tablet daily. Participants were scanned at baseline and 1.9 ± 0.8 yr later. The 3D VWT map at each scanning session was computed by matching the outer wall and lumen surfaces on a point-by-point basis. The 3D annual VWT-Change maps were obtained by first registering the 3D VWT maps obtained at the baseline and follow-up scanning sessions, and then taking the point-wise difference in VWT and dividing the result by the years elapsed from the baseline to the follow-up scanning session. The 3D VWT-Change maps constructed for all patients were mapped to a 2D carotid template to adjust for the anatomic variability of the arteries. A weight at each point of the carotid template was assigned based on the degree of correlation between the VWT-Change measurements exhibited at that point and the treatment received (i.e., B-Vitamin or placebo) quantified by mutual information. The weighted mean of VWT-Change for each patient, denoted by ΔVWT¯Weighted, was computed according to this weight. T-tests were performed to compare the sensitivity of ΔVWT¯Weighted with existing biomarkers in detecting treatment effects. These biomarkers included changes in intima-media thickness (IMT), total plaque area (TPA), vessel wall volume (VWV), unweighted average of VWT-Change (ΔVWT¯) and a previously described biomarker, denoted by ΔVWT¯S, that quantifies the mean VWT-Change specific to regions of interest identified by a feature selection algorithm. RESULTS: Among the six biomarkers evaluated, the effect of B Vitamins was detected only by ΔVWT¯Weighted in this cohort (P=4.4×10-3). The sample sizes per treatment group required to detect an effect as large as exhibited in this study were 139, 178, 41 for ΔVWV, ΔVWT¯ and ΔVWT¯Weighted respectively. CONCLUSION: The proposed weighted mean of VWT-Change is more sensitive than existing biomarkers in detecting treatment effects. This measurement tool will allow for many proof-of-principal studies to be performed for various novel treatments before a more costly study involving a larger population is held to validate the results.