Manifold learning with structured subspace for multi-label feature selection

doi:10.1016/j.patcog.2021.108169

Manifold learning with structured subspace for multi-label feature selection

2021-12-01

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DOI：10.1016/j.patcog.2021.108169

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Nowadays, multi-label learning is ubiquitous in practical applications, in which multi-label data is always confronted with the curse of high-dimensional features. Feature selection has been shown to effectively improve learning performance by selecting discriminative features. Conventional multi-label feature se-lection only focuses on associating input features with corresponding labels while neglecting the potential structural information, i.e., instance correlations and label correlations. To tackle this problem, we pro -pose manifold learning with structured subspace for multi-label feature selection. Specifically, we first uncover a latent subspace for a more compact and accurate data representation, and take advantage of the subspace to explore the correlations among instances. Then, we explore label correlations in manifold learning to guarantee the global and local structural consistency of labels. Besides, l 2 , 1-norm is introduced into loss function and sparse regularization to facilitate feature selection process. A detail optimization algorithm is presented to solve the objective function of the proposed method. Extensive experiments on real-world data show the superiority of the proposed method under various metrics. (c) 2021 Elsevier Ltd. All rights reserved.

Keywords:

Multi-label learning

Feature selection

Manifold learning

Structured subspace

Instance correlations

Label correlations

Manifold learning with structured subspace for multi-label feature selection

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