Low-rank dictionary learning for unsupervised feature selection

doi:10.1016/j.eswa.2022.117149

Low-rank dictionary learning for unsupervised feature selection

2022-09-01

16

OA

AI

M

Mohsen Ghassemi Parsa

DOI：10.1016/j.eswa.2022.117149

Original

Save

View PDF

Abstract

En 中文

There are many high-dimensional data in real-world applications such as biology, computer vision, and social networks. Feature selection approaches are devised to confront high-dimensional data challenges with the aim of efficient learning technologies as well as reduction of models complexity. Due to the hardship of labeling on these datasets, there are a variety of approaches for the feature selection process in an unsupervised setting by considering some important characteristics of data. In this paper, we introduce a novel unsupervised feature selection approach by applying dictionary learning idea in a low-rank representation. Low-rank dictionary learning not only enables us to provide a new data representation but also maintains feature correlation. Then, spectral analysis is employed to preserve sample similarities. Finally, a unified objective function for unsupervised feature selection is proposed in a sparse way by an l2,1-norm regularization. Furthermore, an efficient numerical algorithm is designed to solve the corresponding optimization problem. We demonstrate the performance of the proposed method based on a variety of standard datasets from different applied domains. Our experimental findings reveal that the proposed method outperforms the state-of-the-art algorithms.

Keywords:

Unsupervised feature selection

Dictionary learning

Sparse learning

Spectral analysis

Low-rank representation

Low-rank dictionary learning for unsupervised feature selection

Abstract

AI Summary

Journal

Organization