Maximum Joint Probability With Multiple Representations for Clustering

作     者：Zhang, Rui Zhang, Hongyuan Li, Xuelong 

作者机构：Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS》 (IEEE Trans. Neural Networks Learn. Sys.)

年 卷 期：2022年第33卷第9期

页      面：4300-4310页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Key Research and Development Program of China [2018AAA0102200] National Natural Science Foundation of China [61871470, U1801262, 61761130079] 

主　　题：Kernel Unsupervised learning Data models Clustering algorithms Task analysis Feature extraction Computational modeling Clustering maximum joint probability multikernel learning multiview learning unsupervised learning 

摘      要：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.