4.7 Article

Non-negative multi-label feature selection with dynamic graph constraints

Journal

KNOWLEDGE-BASED SYSTEMS
Volume 238, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.knosys.2021.107924

Keywords

Multi-label learning; Feature selection; Supervised learning; Manifold learning; Laplacian matrix

Funding

  1. Natural Science Foundation of China [61976130]
  2. Key Research and Development Project of Shaanxi Province, China [2018KW-021]
  3. Natural Science Foundation of Shaanxi Province, China [2020JQ-923]

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This paper proposes a non-negative multi-label feature selection method with dynamic graph constraints to address the loss of label information. Experimental results demonstrate the effectiveness of the proposed method.
Feature selection can combat dimension disasters and improve the performance of classification algorithms, so multi-label feature selection is an essential part of multi-label learning and has attracted widespread attention. Many existing multi-label feature selection methods either do not consider the correlation between labels or directly use logical labels to guide the feature selection process, which leads to the loss of label information. This paper proposes a non-negative multi-label feature selection (NMDG) with dynamic graph constraints to address this issue. In the NMDG model, the original data space is projected into a low-dimensional manifold space by linear regression to construct the pseudo label matrix. The pseudo label matrix has the same topological structure as the original data by combining the non-negative constraints and the label graph matrix. Then, the robust low-dimensional space of the pseudo label matrix is used to construct the dynamic graph matrix, which is combined with the feature manifold to guide the learning of the feature weight matrix. Finally, we design an iterative algorithm based on alternating optimization to solve the proposed method and give convergence proof. Experimental results on ten real multi-label data sets compared with seven representative methods show the effectiveness of the proposed method.(c) 2021 Elsevier B.V. All rights reserved.

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