A New Locality-Preserving Canonical Correlation Analysis Algorithm for Multi-View Dimensionality Reduction

Canonical correlation analysis (CCA) is a well-known technique for extracting linearly correlated features from multiple views (i.e., sets of features) of data. Recently, a locality-preserving CCA, named LPCCA, has been developed to incorporate the neighborhood information into CCA. Although LPCCA is proved to be better in revealing the intrinsic data structure than CCA, its discriminative power for subsequent classification is low on high-dimensional data sets such as face databases. In this paper, we propose an alternative formulation for integrating the neighborhood information into CCA and derive a new locality-preserving CCA algorithm called ALPCCA, which can better discover the local manifold structure of data and further enhance the discriminative power for high-dimensional classification. The experimental results on both synthetic and real-world data sets including multiple feature data set and face databases validate the effectiveness of the proposed method.