Deep Multi-View Concept Learning

Cai Xu, Ziyu Guan, Wei Zhao, Yunfei Niu, Quan Wang, Zhiheng Wang
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
Multi-view data is common in real-world datasets, where different views describe distinct perspectives. To better summarize the consistent and complementary information in multi-view data, researchers have proposed various multi-view representation learning algorithms, typically based on factorization models. However, most previous methods were focused on shallow factorization models which cannot capture the complex hierarchical information. Although a deep multi-view factorization model has
more » ... n proposed recently, it fails to explicitly discern consistent and complementary information in multi-view data and does not consider conceptual labels. In this work we present a semi-supervised deep multi-view factorization method, named Deep Multi-view Concept Learning (DMCL). DMCL performs nonnegative factorization of the data hierarchically, and tries to capture semantic structures and explicitly model consistent and complementary information in multi-view data at the highest abstraction level. We develop a block coordinate descent algorithm for DMCL. Experiments conducted on image and document datasets show that DMCL performs well and outperforms baseline methods.
doi:10.24963/ijcai.2018/402 dblp:conf/ijcai/XuGZNWW18 fatcat:7gwo4xw42bhrhgm4r6vdodob2u