Simultaneous Representation Learning and Clustering for Incomplete Multi-view Data
Simultaneous Representation Learning and Clustering for Incomplete Multi-view Data
Wenzhang Zhuge, Chenping Hou, Xinwang Liu, Hong Tao, Dongyun Yi
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Main track. Pages 4482-4488.
https://doi.org/10.24963/ijcai.2019/623
Incomplete multi-view clustering has attracted various attentions from diverse fields. Most existing methods factorize data to learn a unified representation linearly. Their performance may degrade when the relations between the unified representation and data of different views are nonlinear. Moreover, they need post-processing on the unified representations to extract the clustering indicators, which separates the consensus learning and subsequent clustering. To address these issues, in this paper, we propose a Simultaneous Representation Learning and Clustering (SRLC) method. Concretely, SRLC constructs similarity matrices to measure the relations between pair of instances, and learns low-dimensional representations of present instances on each view and a common probability label matrix simultaneously. Thus, the nonlinear information can be reflected by these representations and the clustering results can obtained from label matrix directly. An efficient iterative algorithm with guaranteed convergence is presented for optimization. Experiments on several datasets demonstrate the advantages of the proposed approach.
Keywords:
Machine Learning: Unsupervised Learning
Machine Learning: Multi-instance;Multi-label;Multi-view learning
Machine Learning: Clustering