Unified subspace learning for incomplete and unlabeled multi-view data. (July 2017) - Yin, Qiyue; Wu, Shu; Wang, Liang
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.