ANIMC: A Soft Framework for Auto-weighted Noisy and Incomplete Multi-view Clustering
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| Format: | Preprint |
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2020
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| author | Fang, Xiang Hu, Yuchong Zhou, Pan Wu, Dapeng Oliver |
| author_facet | Fang, Xiang Hu, Yuchong Zhou, Pan Wu, Dapeng Oliver |
| contents | Multi-view clustering has wide applications in many image processing scenarios. In these scenarios, original image data often contain missing instances and noises, which is ignored by most multi-view clustering methods. However, missing instances may make these methods difficult to use directly and noises will lead to unreliable clustering results. In this paper, we propose a novel Auto-weighted Noisy and Incomplete Multi-view Clustering framework (ANIMC) via a soft auto-weighted strategy and a doubly soft regular regression model. Firstly, by designing adaptive semi-regularized nonnegative matrix factorization (adaptive semi-RNMF), the soft auto-weighted strategy assigns a proper weight to each view and adds a soft boundary to balance the influence of noises and incompleteness. Secondly, by proposingθ-norm, the doubly soft regularized regression model adjusts the sparsity of our model by choosing differentθ. Compared with existing methods, ANIMC has three unique advantages: 1) it is a soft algorithm to adjust our framework in different scenarios, thereby improving its generalization ability; 2) it automatically learns a proper weight for each view, thereby reducing the influence of noises; 3) it performs doubly soft regularized regression that aligns the same instances in different views, thereby decreasing the impact of missing instances. Extensive experimental results demonstrate its superior advantages over other state-of-the-art methods. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2011_10331 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | ANIMC: A Soft Framework for Auto-weighted Noisy and Incomplete Multi-view Clustering Fang, Xiang Hu, Yuchong Zhou, Pan Wu, Dapeng Oliver Computer Vision and Pattern Recognition Machine Learning Multi-view clustering has wide applications in many image processing scenarios. In these scenarios, original image data often contain missing instances and noises, which is ignored by most multi-view clustering methods. However, missing instances may make these methods difficult to use directly and noises will lead to unreliable clustering results. In this paper, we propose a novel Auto-weighted Noisy and Incomplete Multi-view Clustering framework (ANIMC) via a soft auto-weighted strategy and a doubly soft regular regression model. Firstly, by designing adaptive semi-regularized nonnegative matrix factorization (adaptive semi-RNMF), the soft auto-weighted strategy assigns a proper weight to each view and adds a soft boundary to balance the influence of noises and incompleteness. Secondly, by proposingθ-norm, the doubly soft regularized regression model adjusts the sparsity of our model by choosing differentθ. Compared with existing methods, ANIMC has three unique advantages: 1) it is a soft algorithm to adjust our framework in different scenarios, thereby improving its generalization ability; 2) it automatically learns a proper weight for each view, thereby reducing the influence of noises; 3) it performs doubly soft regularized regression that aligns the same instances in different views, thereby decreasing the impact of missing instances. Extensive experimental results demonstrate its superior advantages over other state-of-the-art methods. |
| title | ANIMC: A Soft Framework for Auto-weighted Noisy and Incomplete Multi-view Clustering |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2011.10331 |