期刊
INFORMATION FUSION
卷 55, 期 -, 页码 251-259出版社
ELSEVIER
DOI: 10.1016/j.inffus.2019.09.005
关键词
Clustering; Multi-view; Majorization-Minimization; Nonnegative matrix factorization
资金
- National Natural Science Foundation of China [61772427, 61751202, 61936014]
- National Key Research and Development Program of China [2018YFB1403500]
- Fundamental Research Funds for the Central Universities [G2019KY0501]
The application of most existing multi-view spectral clustering methods is generally limited by the following three deficiencies. First, the requirement to post-processing, such as K-means or spectral rotation. Second, the susceptibility to parameter selection. Third, the high computation cost. To this end, in this paper we develop a novel method that integrates nonnegative embedding and spectral embedding into a unified framework. Two promising advantages of proposed method include 1) the learned nonnegative embedding directly reveals the consistent clustering result, such that the uncertainty brought by post-processing can be avoided; 2) the involved model is parameter-free, which makes our method more applicable than existing algorithms that introduce many additional parameters. Furthermore, we develop an efficient inexact Majorization-Minimization method to solve the involved model which is non-convex and non-smooth. Experiments on multiple benchmark datasets demonstrate that our method achieves state-of-the-art performance.
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