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Adaptive sparse graph learning for multi-view spectral clustering

Adaptive sparse graph learning for multi-view spectral clustering In recent years, graph-based multi-view spectral clustering methods have achieved remarkable progress. However, most of which are generally deficient in the following two aspects. First, ignoring the different importance of multiple views, low-quality views in the multi-view data may seriously affect the clustering performance. Second, for constructed graphs, noise and outliers are difficult to effectively filter out. For the purpose of overcoming the above two deficiencies, this paper proposes a novel adaptive sparse graph learning for multi-view spectral clustering (ASGL) method. Specifically, the adaptive neighbor graph learning method is adopted to construct the similarity matrices of all views, which improves the robustness to noise and outliers. By adaptively assigning the weight of each view, the complementary information between the views is combined to more accurately describe the essential category attributes between the sample data. An effective algorithm for solving the optimization problem of ASGL model is proposed. Compared to several state-of-the-art algorithms, extensive experiments on several benchmark datasets verify good clustering performance of ASGL. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Intelligence Springer Journals

Adaptive sparse graph learning for multi-view spectral clustering

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References (44)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
0924-669X
eISSN
1573-7497
DOI
10.1007/s10489-022-04267-9
Publisher site
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Abstract

In recent years, graph-based multi-view spectral clustering methods have achieved remarkable progress. However, most of which are generally deficient in the following two aspects. First, ignoring the different importance of multiple views, low-quality views in the multi-view data may seriously affect the clustering performance. Second, for constructed graphs, noise and outliers are difficult to effectively filter out. For the purpose of overcoming the above two deficiencies, this paper proposes a novel adaptive sparse graph learning for multi-view spectral clustering (ASGL) method. Specifically, the adaptive neighbor graph learning method is adopted to construct the similarity matrices of all views, which improves the robustness to noise and outliers. By adaptively assigning the weight of each view, the complementary information between the views is combined to more accurately describe the essential category attributes between the sample data. An effective algorithm for solving the optimization problem of ASGL model is proposed. Compared to several state-of-the-art algorithms, extensive experiments on several benchmark datasets verify good clustering performance of ASGL.

Journal

Applied Intelligence – Springer Journals

Published: Jun 1, 2023

Keywords: Multi-view spectral clustering; Adaptive sparse graph; Sparse representation; Consensus similarity matrix

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