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Robust anchor-based multi-view clustering via spectral embedded concept factorization
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DOI：10.1016/j.neucom.2023.01.028.png)
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Multi-view clustering (MVC) often provides superior effectiveness to single-view clustering due to the integration of information from diverse views. Nonetheless, existing MVC methods are limited to large-scale real-world data by the drawbacks of low efficiency and poor robustness. To address these issues, we propose a novel robust anchor-based MVC model via spectral embedded concept factorization (RAMCSF). RAMCSF builds anchor graphs to approximate full-sample graphs and decomposes these anchor graphs by concept factorization (CF). To improve the clustering effectiveness, factor matrices of CF are constrained as orthogonal matrices to reduce the freedom of decomposition, and a novel small-scale anchor-based spectral embedding is designed to explore the high-order neighbor relationships. To restrain complex noises distributed in real-world data, we employ correntropy to measure the error between the original data and the learned representation. Moreover, RAMCSF can get a clustering indi-cator matrix directly, avoiding additional post-processing and ensuring that changes in data dimensions have a limited impact on efficiency. The model is then optimized by a novel fast half-quadratic-based optimization strategy that combines the orthogonal properties and the traces of matrices. Extensive experiments indicate that RAMCSF can achieve higher efficiency and robustness while maintaining com-parable effectiveness to other state-of-the-art methods.(C) 2023 Published by Elsevier B.V.
Keyword:
Multi-view clustering
Concept factorization
Anchor graph
Correntropy
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
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