Data clustering based on label consistent constraint matrix factorization

Xianzhong Long

doi:10.1117/12.2503137

9 August 2018 Data clustering based on label consistent constraint matrix factorization

Xianzhong Long

Proceedings Volume 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018); 108066W (2018) https://doi.org/10.1117/12.2503137
Event: Tenth International Conference on Digital Image Processing (ICDIP 2018), 2018, Shanghai, China

Abstract

The schemes based on matrix factorization have been applied in many fields, such as recommender system, image classification, data clustering, face recognition and so on. Among them, non negative matrix factorization (NMF) and concept factorization (CF) are the two most commonly used matrix decomposition techniques. NMF decomposes a matrix into the product of two non negative matrices and CF divides a matrix into the product of three matrices. CF is considered as one variant of NMF. The biggest difference between them is that CF can be executed in a kernel space. The development of supervised learning methods show that label information is critical to enhance the model’s ability. In this paper, NMF and CF based on label consistent constraint methods are presented respectively for data clustering. The corresponding multiplicative update solutions, parameters selection and convergence verification are given. Clustering results on three data sets reveal that our methods outperform the state-of-the-art algorithms in terms of accuracy and normalized mutual information.

Citation Download Citation

Xianzhong Long "Data clustering based on label consistent constraint matrix factorization", Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108066W (9 August 2018); https://doi.org/10.1117/12.2503137

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