Partition-Level Tensor Learning-Based Multiview Unsupervised Feature Selection

Abstract

Multiview unsupervised feature selection is an emerging direction in the machine learning community because of its ability to identify informative patterns and reduce the dimensionality of multiview data. Although numerous methods have been proposed and shown to be effective, they have some limitations: 1) most existing algorithms fail to improve the model performance along the view dimension; 2) they rarely incorporate more discriminative partition information; and 3) the negative effects of marginal samples are not considered. To solve these problems, we propose a novel method termed as partition-level tensor learning-based multiview unsupervised feature selection (PTFS). The proposed method optimizes a low-rank constrained tensor assembled by the inner product of base partition matrices. By doing so, PTFS simultaneously leverages the high-order view correlation and indirectly integrates discriminative partition information. Besides, a statistic-based adaptive self-paced strategy is introduced to ensure that confident samples are prioritized for training the model. Moreover, an effective alternating optimization method is designed to solve the resulting optimization problem. Extensive experiments on ten datasets demonstrate the effectiveness and efficiency of the proposed method compared to the state-of-the-art methods. The code is available at https://github.com/HdTgon/2023-TNNLS-PTFS.