Adaptive multi-view clustering based on nonnegative matrix factorization and pairwise co-regularization

Tianzhen Zhang; Xiumei Wang; Xinbo Gao

doi:10.1117/12.2304803

10 April 2018 Adaptive multi-view clustering based on nonnegative matrix factorization and pairwise co-regularization

Tianzhen Zhang, Xiumei Wang, Xinbo Gao

Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 106152Y (2018) https://doi.org/10.1117/12.2304803
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China

Abstract

Nowadays, several datasets are demonstrated by multi-view, which usually include shared and complementary information. Multi-view clustering methods integrate the information of multi-view to obtain better clustering results. Nonnegative matrix factorization has become an essential and popular tool in clustering methods because of its interpretation. However, existing nonnegative matrix factorization based multi-view clustering algorithms do not consider the disagreement between views and neglects the fact that different views will have different contributions to the data distribution. In this paper, we propose a new multi-view clustering method, named adaptive multi-view clustering based on nonnegative matrix factorization and pairwise co-regularization. The proposed algorithm can obtain the parts-based representation of multi-view data by nonnegative matrix factorization. Then, pairwise co-regularization is used to measure the disagreement between views. There is only one parameter to auto learning the weight values according to the contribution of each view to data distribution. Experimental results show that the proposed algorithm outperforms several state-of-the-arts algorithms for multi-view clustering.

Citation Download Citation

Tianzhen Zhang, Xiumei Wang, and Xinbo Gao "Adaptive multi-view clustering based on nonnegative matrix factorization and pairwise co-regularization", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106152Y (10 April 2018); https://doi.org/10.1117/12.2304803

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KEYWORDS

Matrices

Data mining

Electronics engineering

Image processing

Internet

Machine learning

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