Locality cross-view regression for feature extraction

Regression-based methods (RBMs) have become a widely-used technique for feature extraction. However, most RBMs are only suitable for single-view data and fail to explore the consistency and complementarity information from multiple views. In this paper, we firstly propose a unified framework called locality cross-view regression (ULCR) to realize multi-view feature extraction. ULCR utilizes a regression loss function to explore the relationship between different views, meanwhile, preserving the manifold structure of samples. Then, under the ULCR framework, we propose a standard LCR (SLCR) which utilizes F-norm as the metric. SLCR is convenient for solving, but sensitive to the outliers. Therefore, a robust locality cross-view regression (RLCR) is proposed which uses L2,1-norm instead of F-norm in SLCR. The convergence analysis of the algorithm and the relationship between SLCR and RLCR are discussed. Experiment results on image datasets illustrate that the proposed methods develop better performance than other related methods.