Abstract
Multi-view correlation learning has attracted great attention with the proliferation of heterogeneous data. Typical methods, such as Canonical Correlation Analysis (CCA) and its variants, usually maximize one-to-one corresponding correlation of inter-view data, while most of them neglect discriminative multi-label information and local structure of each view data. In this paper, we propose multi-label Semantics and Locality Preserving Correlation Projections method (SLPCP), which seeks for a semantic common subspace by jointly learning view-specific linear projections from intra-view and interview perspectives simultaneously. SLPCP can be easily optimized with generalized eigen value decomposition via concatenating the projections of multi-views. Applied to retrieval tasks of image and text data in experiments, SLPCP out performs state-of-the-art methods on a widely used dataset NUS-WIDE. The extensive experiments also validate that it is effective to preserve the multi-label semantics and locality of multi-view data.