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Neural Processing Letters
Erschienen in: Neural Processing Letters 1/2019

03.03.2018

Learning Robust Weighted Group Sparse Graph for Discriminant Visual Analysis

verfasst von: Tan Guo, Xiaoheng Tan, Lei Zhang, Qin Liu, Lu Deng, Chaochen Xie

Erschienen in: Neural Processing Letters | Ausgabe 1/2019

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Abstract

Recently, sparse representation (SR) based graph has been successfully applied for dimensionality reduction (DR). However, the unsupervised characteristic of SR may cause instable representation results, which is undesired for graph construction. To alleviate the problem, a robust weighted group sparse representation (RWGSR) method is developed by minimizing the combination of l1-norm regularized representation fidelity and the weighted l2,1-norm regularized representation coefficients. RWGSR can find the robust and stable intrinsic intra-class and inter-class adjacent relations of samples. The intra-class and inter-class representations of RWGSR are then utilized to construct corresponding intra-class and inter-class graphs. With the graphs, a novel supervised DR algorithm named robust weighted group sparse graph based embedding (RWGSE) is proposed. Benefitting from RWGSR, RWGSE considers both intra-class and inter-class intrinsic structures of data, and seeks a low-dimensional subspace by simultaneously minimizing the intra-class scatter and maximizing the inter-class scatter. Extensive experiments on public benchmark face and object datasets show the effectiveness of the proposed method.
Vorheriger Artikel Global Exponential Synchronization of Complex-Valued Neural Networks with Time Delays via Matrix Measure Method
Nächster Artikel Weighted Version of Extended Nearest Neighbors

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Metadaten
Titel
Learning Robust Weighted Group Sparse Graph for Discriminant Visual Analysis
verfasst von
Tan Guo
Xiaoheng Tan
Lei Zhang
Qin Liu
Lu Deng
Chaochen Xie
Publikationsdatum
03.03.2018
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 1/2019
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-018-9809-5

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