Unified subspace learning for incomplete and unlabeled multi-view data
作者：
Highlights：
• Class indicator matrix is learned for incomplete and unlabeled multi-view data.
• Preserving the inter-view and intra-view data similarity can improve performance.
• Running time is in the same magnitudes with that of the mainstream methods.
• Obtain best results for incomplete multi-view clustering and cross-modal retrieval.
摘要
•Class indicator matrix is learned for incomplete and unlabeled multi-view data.•Preserving the inter-view and intra-view data similarity can improve performance.•Running time is in the same magnitudes with that of the mainstream methods.•Obtain best results for incomplete multi-view clustering and cross-modal retrieval.
论文关键词：Multi-view learning,Subspace learning,Incomplete and unlabeled data,Multi-view clustering,Cross-modal retrieval
论文评审过程：Received 11 August 2016, Revised 15 January 2017, Accepted 19 January 2017, Available online 17 February 2017, Version of Record 1 March 2017.
论文官网地址：https://doi.org/10.1016/j.patcog.2017.01.035
