期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 33, 期 9, 页码 4300-4310出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2021.3056420
关键词
Kernel; Unsupervised learning; Data models; Clustering algorithms; Task analysis; Feature extraction; Computational modeling; Clustering; maximum joint probability; multikernel learning; multiview learning; unsupervised learning
类别
资金
- National Key Research and Development Program of China [2018AAA0102200]
- National Natural Science Foundation of China [61871470, U1801262, 61761130079]
The article proposes a new clustering framework that aims to maximize the joint probability of data and parameters, and can use a prior distribution to measure the rationality of different representations.
Classical generative models in unsupervised learning intend to maximize p(X). In practice, samples may have multiple representations caused by various transformations, measurements, and so on. Therefore, it is crucial to integrate information from different representations, and lots of models have been developed. However, most of them fail to incorporate the prior information about data distribution p(X) to distinguish representations. In this article, we propose a novel clustering framework that attempts to maximize the joint probability of data and parameters. Under this framework, the prior distribution can be employed to measure the rationality of diverse representations. K-means is a special case of the proposed framework. Meanwhile, a specific clustering model considering both multiple kernels and multiple views is derived to verify the validity of the designed framework and model.
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