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发表刊物：AAAI Conference on Artificial Intelligence (AAAI-20)

关键字：Multi-view clustering, Representation learning, Generative model, Mixture of Gaussian, Large-scale clustering

摘要：Clustering multi-view data has been a fundamental research topic in the computer vision community. It has been shown that a better accuracy can be achieved by integrating information of all the views than just using one view individually. However, the existing methods often struggle with the issues of dealing with the large-scale datasets and the poor performance in reconstructing samples. This paper proposes a novel multi-view clustering method by learning a shared generative latent representation that obeys a mixture of Gaussian distributions. The motivation is based on the fact that the multi-view data share a common latent embedding despite the diversity among the views. Specifically, benefited from the success of the deep generative learning, the proposed model not only can extract the nonlinear features from the views, but render a powerful ability in capturing the correlations among all the views. The extensive experimental results, on several datasets with different scales, demonstrate that the proposed method outperforms the state-of-the-art methods under a range of performance criteria.

合写作者：Weitian Huang,Junbin Gao

第一作者：Ming Yin

论文类型：会议论文

页面范围：2020. (CCF A类会议)

是否译文：否

发表时间：2020-02-11