Parameter-free entropy-regularized multi-view clustering with hierarchical feature selection
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913979223244800 |
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| author | Sinaga, Kristina P. Colantonio, Sara Yang, Miin-Shen |
| author_facet | Sinaga, Kristina P. Colantonio, Sara Yang, Miin-Shen |
| contents | Multi-view clustering faces critical challenges in automatically discovering patterns across heterogeneous data while managing high-dimensional features and eliminating irrelevant information. Traditional approaches suffer from manual parameter tuning and lack principled cross-view integration mechanisms. This work introduces two complementary algorithms: AMVFCM-U and AAMVFCM-U, providing a unified parameter-free framework. Our approach replaces fuzzification parameters with entropy regularization terms that enforce adaptive cross-view consensus. The core innovation employs signal-to-noise ratio based regularization ($δ_j^h = \frac{\bar{x}_j^h}{(σ_j^h)^2}$) for principled feature weighting with convergence guarantees, coupled with dual-level entropy terms that automatically balance view and feature contributions. AAMVFCM-U extends this with hierarchical dimensionality reduction operating at feature and view levels through adaptive thresholding ($θ^{h^{(t)}} = \frac{d_h^{(t)}}{n}$). Evaluation across five diverse benchmarks demonstrates superiority over 15 state-of-the-art methods. AAMVFCM-U achieves up to 97% computational efficiency gains, reduces dimensionality to 0.45% of original size, and automatically identifies critical view combinations for optimal pattern discovery. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_05504 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Parameter-free entropy-regularized multi-view clustering with hierarchical feature selection Sinaga, Kristina P. Colantonio, Sara Yang, Miin-Shen Machine Learning Computer Vision and Pattern Recognition Statistics Theory 62H30, 68T05, 68T09, 62H25, 94A17 Multi-view clustering faces critical challenges in automatically discovering patterns across heterogeneous data while managing high-dimensional features and eliminating irrelevant information. Traditional approaches suffer from manual parameter tuning and lack principled cross-view integration mechanisms. This work introduces two complementary algorithms: AMVFCM-U and AAMVFCM-U, providing a unified parameter-free framework. Our approach replaces fuzzification parameters with entropy regularization terms that enforce adaptive cross-view consensus. The core innovation employs signal-to-noise ratio based regularization ($δ_j^h = \frac{\bar{x}_j^h}{(σ_j^h)^2}$) for principled feature weighting with convergence guarantees, coupled with dual-level entropy terms that automatically balance view and feature contributions. AAMVFCM-U extends this with hierarchical dimensionality reduction operating at feature and view levels through adaptive thresholding ($θ^{h^{(t)}} = \frac{d_h^{(t)}}{n}$). Evaluation across five diverse benchmarks demonstrates superiority over 15 state-of-the-art methods. AAMVFCM-U achieves up to 97% computational efficiency gains, reduces dimensionality to 0.45% of original size, and automatically identifies critical view combinations for optimal pattern discovery. |
| title | Parameter-free entropy-regularized multi-view clustering with hierarchical feature selection |
| topic | Machine Learning Computer Vision and Pattern Recognition Statistics Theory 62H30, 68T05, 68T09, 62H25, 94A17 |
| url | https://arxiv.org/abs/2508.05504 |