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Structured optimal graph based sparse feature extraction for semi-supervised learning
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DOI：10.1016/j.sigpro.2020.107456.png)
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Graph-based feature extraction is an efficient technique for data dimensionality reduction, and it has gained intensive attention in various fields such as image processing, pattern recognition, and machine learning. However, conventional graph-based dimensionality reduction algorithms usually depend on a fixed weight graph called similarity matrix, which seriously affects the subsequent feature extraction process. In this paper, a novel structured optimal graph based sparse feature extraction (SOGSFE) method for semi-supervised learning is proposed. In the proposed method, the local structure learning, sparse representation, and label propagation are simultaneously framed to perform data dimensionality reduction. In particular the similarity matrix and the projection matrix are obtained by an iterative calculation manner. The experimental results on several public image datasets demonstrate the robustness and effectiveness of the proposed method. (C) 2020 Elsevier B.V. All rights reserved.
Keyword:
Feature extraction
Semi-supervised learning
Graph construction
Sparse representation
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