Consistent and Complementary Graph Regularized Multi-view Subspace Clustering

• URL: http://arxiv.org/abs/2004.03106v1
• Date: Tue, 7 Apr 2020 03:48:08 GMT
• Title: Consistent and Complementary Graph Regularized Multi-view Subspace Clustering
• Authors: Qinghai Zheng, Jihua Zhu, Zhongyu Li, Shanmin Pang, Jun Wang, Lei Chen
• Abstract summary: This study investigates the problem of multi-view clustering, where multiple views contain consistent information and each view also includes complementary information. We propose a method that involves consistent and complementary graph-regularized multi-view subspace clustering (GRMSC) The objective function is optimized by the augmented Lagrangian multiplier method in order to achieve multi-view clustering.
• Score: 31.187031653119025
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study investigates the problem of multi-view clustering, where multiple views contain consistent information and each view also includes complementary information. Exploration of all information is crucial for good multi-view clustering. However, most traditional methods blindly or crudely combine multiple views for clustering and are unable to fully exploit the valuable information. Therefore, we propose a method that involves consistent and complementary graph-regularized multi-view subspace clustering (GRMSC), which simultaneously integrates a consistent graph regularizer with a complementary graph regularizer into the objective function. In particular, the consistent graph regularizer learns the intrinsic affinity relationship of data points shared by all views. The complementary graph regularizer investigates the specific information of multiple views. It is noteworthy that the consistent and complementary regularizers are formulated by two different graphs constructed from the first-order proximity and second-order proximity of multiple views, respectively. The objective function is optimized by the augmented Lagrangian multiplier method in order to achieve multi-view clustering. Extensive experiments on six benchmark datasets serve to validate the effectiveness of the proposed method over other state-of-the-art multi-view clustering methods.

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