Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Multi-label feature selection based on logistic regression and manifold learning

Version 1 : Received: 14 July 2021 / Approved: 15 July 2021 / Online: 15 July 2021 (08:15:00 CEST)

How to cite: Zhang, Y.; Ma, Y.; Yang, X. Multi-label feature selection based on logistic regression and manifold learning. Preprints 2021, 2021070341 (doi: 10.20944/preprints202107.0341.v1). Zhang, Y.; Ma, Y.; Yang, X. Multi-label feature selection based on logistic regression and manifold learning. Preprints 2021, 2021070341 (doi: 10.20944/preprints202107.0341.v1).

Abstract

Like traditional single label learning, multi-label learning is also faced with the problem of dimensional disaster.Feature selection is an effective technique for dimensionality reduction and learning efficiency improvement of high-dimensional data. In this paper, Logistic regression, manifold learning and sparse regularization were combined to construct a joint framework for multi-label feature selection (LMFS). Firstly, the sparsity of the eigenweight matrix is constrained by the $L_{2,1}$-norm. Secondly, the feature manifold and label manifold can constrain the feature weight matrix to make it fit the data information and label information better. An iterative updating algorithm is designed and the convergence of the algorithm is proved.Finally, the LMFS algorithm is compared with DRMFS, SCLS and other algorithms on eight classical multi-label data sets. The experimental results show the effectiveness of LMFS algorithm.

Keywords

feature selection; manifold learning; multi-label learning; $L_{2,1}$-norm; logistic regression

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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