Abstract
Nowadays, multi-view clustering has attracted more and more attention, which provides a way to partition multi-view data into their corresponding clusters. Previous studies assume that each data instance appears in all views. However, in real-world applications, it is common that each view may contain some missing data instances, resulting in incomplete multi-view data. To address the incomplete multi-view clustering problem, we will propose an auto-weighted incomplete multi-view clustering method in this paper, which learns a common representation of the instances and an affinity matrix of the learned representation simultaneously in a unified framework. Learning the affinity matrix of the representation guides to learn a more discriminative and compact consensus representation for clustering. Moreover, by considering the impact of the significance of different views, an adaptive weighting strategy is designed to measure the importance of each view. An efficient iterative algorithm is proposed to optimize the objective function. Experimental results on various real-world datasets show that the proposed method can improve the clustering performance in comparison with the state-of-the-art methods in most cases.
Highlights
In recent years, many real-world datasets naturally come from multiple sources or comprise of multiple modalities, which are called multi-view data
VOLUME 8, 2020 common representation is obtained which is beneficial to clustering
PROPOSED METHOD we present the proposed approach (AWIMVC), which simultaneously learns a common representation in the latent subspace and an affinity matrix of the learned representation
Summary
Many real-world datasets naturally come from multiple sources or comprise of multiple modalities, which are called multi-view data. In multi-view data, these multiple views provide consistent and complementary information. By exploiting the information present in the different views, multi-view learning methods have been proposed for tasks such as clustering and classification. In all tasks of multi-view learning [1], [2], multi-view clustering [3], which exploits multiple views to effectively learn from unlabeled data, has attracted more and more attention. In the past few years, a number of multi-view clustering methods have been proposed. Among these methods, there are two main clustering categories: spectral based and subspace based. With the help of some similarity measure between examples, spectral clustering [4] has been extended to multi-view data. With the help of some similarity measure between examples, spectral clustering [4] has been extended to multi-view data. de Sa [5]
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