Semantic-constraint graph dual non-negative matrix factorization in text co-clustering

Yu Liu; Jiaxun Hua; Youguang Chen

doi:10.1117/12.2541938

27 November 2019 Semantic-constraint graph dual non-negative matrix factorization in text co-clustering

Yu Liu, Jiaxun Hua, Youguang Chen

Proceedings Volume 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence; 113211Y (2019) https://doi.org/10.1117/12.2541938
Event: The Second International Conference on Image, Video Processing and Artifical Intelligence, 2019, Shanghai, China

Abstract

Co-clustering, an extension of one-sided clustering, refers to process of clustering data points and features simultaneously. During text clustering tasks, traditional one-sided clustering algorithms have encountered difficulties dealing with sparse problem. Instead, a co-clustering procedure, where data's common organizing form is a big matrix aggregated by data points, has proved more useful when faced with sparsity. Based on the traditional co-clustering approaches, a new model named SC-DNMF, which takes into account the semantic constraints between words, is proposed in this paper. Experiments on several datasets indicate that our proposal improves the clustering accuracy over traditional co-clustering models.

Citation Download Citation

Yu Liu, Jiaxun Hua, and Youguang Chen "Semantic-constraint graph dual non-negative matrix factorization in text co-clustering", Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 113211Y (27 November 2019); https://doi.org/10.1117/12.2541938

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