Efficient and Stable Unsupervised Feature Selection Based on Novel Structured Graph and Data Discrepancy Learning.

Unsupervised feature selection is an important tool in data mining, machine learning, and pattern recognition. Although data labels are often missing, the number of data classes can be known and exploited in many scenarios. Therefore, a structured graph, whose number of connected components is identical to the number of data classes, has been proposed and is frequently applied in unsupervised feature selection. However, methods based on the structured graph learning face two problems. First, their structured graphs are not always guaranteed to maintain the same number of connected components as the data classes with existing optimization algorithms. Second, they usually lack strategies for choosing moderate hyperparameters. To solve these problems, an efficient and stable unsupervised feature selection method based on a novel structured graph and data discrepancy learning (ESUFS) is proposed. Specifically, the novel structured graph, consisting of a pairwise data similarity matrix and an indicator matrix, can be efficiently learned by solving a discrete optimization problem. Data discrepancy learning focuses on features that maximize the difference among data and helps in selecting discriminative features. Extensive experiments conducted on various datasets show that ESUFS outperforms state-of-the-art methods not only in accuracy (ACC) but also in stability and speed.

Adaptive Density Spatial Clustering Method Fusing Chameleon Swarm Algorithm.

The density-based spatial clustering of application with noise (DBSCAN) algorithm is able to cluster arbitrarily structured datasets. However, the clustering result of this algorithm is exceptionally sensitive to the neighborhood radius (Eps) and noise points, and it is hard to obtain the best result quickly and accurately with it. To solve the above problems, we propose an adaptive DBSCAN method based on the chameleon swarm algorithm (CSA-DBSCAN). First, we take the clustering evaluation index of the DBSCNA algorithm as the objective function and use the chameleon swarm algorithm (CSA) to iteratively optimize the evaluation index value of the DBSCAN algorithm to obtain the best Eps value and clustering result. Then, we introduce the theory of deviation in the data point spatial distance of the nearest neighbor search mechanism to assign the identified noise points, which solves the problem of over-identification of the algorithm noise points. Finally, we construct color image superpixel information to improve the CSA-DBSCAN algorithm's performance regarding image segmentation. The simulation results of synthetic datasets, real-world datasets, and color images show that the CSA-DBSCAN algorithm can quickly find accurate clustering results and segment color images effectively. The CSA-DBSCAN algorithm has certain clustering effectiveness and practicality.