Flexible Multi-View Representation Learning for Subspace Clustering
Flexible Multi-View Representation Learning for Subspace Clustering
Ruihuang Li, Changqing Zhang, Qinghua Hu, Pengfei Zhu, Zheng Wang
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Main track. Pages 2916-2922.
https://doi.org/10.24963/ijcai.2019/404
In recent years, numerous multi-view subspace clustering methods have been proposed to exploit the complementary information from multiple views. Most of them perform data reconstruction within each single view, which makes the subspace representation unpromising and thus can not well identify the underlying relationships among data. In this paper, we propose to conduct subspace clustering based on Flexible Multi-view Representation (FMR) learning, which avoids using partial information for data reconstruction. The latent representation is flexibly constructed by enforcing it to be close to different views, which implicitly makes it more comprehensive and well-adapted to subspace clustering. With the introduction of kernel dependence measure, the latent representation can flexibly encode complementary information from different views and explore nonlinear, high-order correlations among these views. We employ the Alternating Direction Minimization (ADM) method to solve our problem. Empirical studies on real-world datasets show that our method achieves superior clustering performance over other state-of-the-art methods.
Keywords:
Machine Learning: Multi-instance;Multi-label;Multi-view learning
Machine Learning: Clustering