Abstract
In recent years, multiple view clustering tasks has attracted sustained concern as numerous realistic data are composed of distinct expressions. The point is to adequately integrate knowledge from multiple perspectives for facilitating the clustering tasks. The non-negative matrix factorization (NMF) technique is extensively applied in multiple perspective clustering on account of possessing the ability of dimensionality reduction. This paper introduces an innovative NMF multi-view clustering approach, which can extract complementary and compatible information presented in multiple perspective data. Besides, for solving the non-convex optimal issue of the objective function presented in this paper, an available iterative renewing approach is introduced. Experiments on two standard data sets indicate the advantages of our raised algorithm contrasted with basic methods.
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Chai, B. (2022). A Joint Approach Based on Matrix Factorization for Multi-view Clustering. In: Hung, J.C., Yen, N.Y., Chang, JW. (eds) Frontier Computing. FC 2021. Lecture Notes in Electrical Engineering, vol 827. Springer, Singapore. https://doi.org/10.1007/978-981-16-8052-6_43
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DOI: https://doi.org/10.1007/978-981-16-8052-6_43
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