Dual space latent representation learning for unsupervised feature selection

In real-world applications, data instances are not only related to high-dimensional features, but also interconnected with each other. However, the interconnection information has not been fully exploited for feature selection. To address this issue, we propose a novel feature selection algorithm, called dual space latent representation learning for unsupervised feature selection (DSLRL), which exploits the internal association information of data space and feature space to guide feature selection. Firstly, based on latent representation learning in data space, DSLRL produces dual space latent representation learning, which characterizes the inherent structure of data space and feature space, respectively. Secondly, in order to overcome the problem of the lack of label information, DSLRL optimizes the low-dimensional latent representation matrix of data space as a pseudo-label matrix to provide clustering indicators. Moreover, the latent representation matrix of feature space is unified with the transformation matrix to benefit the matching of the data matrix and the clustering indicator matrix. In addition, DSLRL uses non-negative and orthogonal conditions to constrain the sparse transform matrix, making it more accurate for evaluating features. Finally, an alternating method is employed to optimize the objective function. Compared with seven state-of-the-art algorithms, experimental results on twelve datasets show the effectiveness of DSLRL. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

The DSLRL algorithm leverages internal association information in data space and feature space for guiding feature selection. In the absence of label information, it optimizes a low-dimensional latent representation matrix of data space to provide clustering indicators, and uses non-negative and orthogonal conditions to constrain the sparse transform matrix for more accurate feature evaluation.