Sparse and low-rank feature selection for multi-label learning (다중레이블 학습을 위한 희소성과 저랭크 기반 특징 선별 기법)

Hyunki Lim (임현기)

doi:10.9708/jksci.2021.26.07.001

Sparse and low-rank feature selection for multi-label learning

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2021, 26(7), pp.1-7
DOI : 10.9708/jksci.2021.26.07.001
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : April 19, 2021
Accepted : May 26, 2021
Published : July 30, 2021

Hyunki Lim ¹

¹경기대학교

Accredited

ABSTRACT

In this paper, we propose a feature selection technique for multi-label classification. Many existing feature selection techniques have selected features by calculating the relation between features and labels such as a mutual information scale. However, since the mutual information measure requires a joint probability, it is difficult to calculate the joint probability from an actual premise feature set. Therefore, it has the disadvantage that only a few features can be calculated and only local optimization is possible. Away from this regional optimization problem, we propose a feature selection technique that constructs a low-rank space in the entire given feature space and selects features with sparsity. To this end, we designed a regression-based objective function using Nuclear norm, and proposed an algorithm of gradient descent method to solve the optimization problem of this objective function. Based on the results of multi-label classification experiments on four data and three multi-label classification performance, the proposed methodology showed better performance than the existing feature selection technique. In addition, it was showed by experimental results that the performance change is insensitive even to the parameter value change of the proposed objective function.

KEYWORDS

Multi-label learning, feature selection, low-rank approximation, sparsity, optimization method

Citation status

* References for papers published after 2023 are currently being built.

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This paper was written with support from the National Research Foundation of Korea.

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Sparse and low-rank feature selection for multi-label learning

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* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Sparse and low-rank feature selection for multi-label learning

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* References for papers published after 2023 are currently being built.