Unified subspace learning for incomplete and unlabeled multi-view data. (July 2017)

• Record Type: Journal Article
• Authors: Yin, Qiyue; Wu, Shu; Wang, Liang
• Is Part Of: Pattern recognition. Volume 67(2017:Jul.)
• Page Start: 313
• Page End: 327
• Publication Date: 2017-07
• Subjects: Multi-view learning -- Subspace learning -- Incomplete and unlabeled data -- Multi-view clustering -- Cross-modal retrieval; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2017.01.035
• Languages: English
• ISSNs: 0031-3203

Abstract

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. Abstract: Multi-view data with each view corresponding to a type of feature set are common in real world. Usually, previous multi-view learning methods assume complete views. However, multi-view data are often incomplete, namely some samples have incomplete feature sets. Besides, most data are unlabeled due to a large cost of manual annotation, which makes learning of such data a challenging problem. In this paper, we propose a novel subspace learning framework for incomplete and unlabeled multi-view data. The model directly optimizes the class indicator matrix, which establishes a bridge for incomplete feature sets. Besides, feature selection is considered to deal with high dimensional and noisy features. Furthermore, the inter-view and intra-view data similarities are preserved to enhance the model. To these ends, an objective is developed along with an efficient optimization strategy. Finally, extensive experiments are conducted for multi-view clustering and cross-modal retrieval, achieving the state-of-the-art performance under various settings.