Two Variations on Fisher's Linear Discriminant for Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Locality preserving CCA with applications to data visualization and pose estimation
Image and Vision Computing
Journal of Cognitive Neuroscience
A Novel Method of Combined Feature Extraction for Recognition
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Learning with l1-graph for image analysis
IEEE Transactions on Image Processing
Robust sparse coding for face recognition
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
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In this paper, a kernel based sparse locality preserving canonical correlation analysis (KSLPCCA) method is presented for high dimensional feature extraction. Unlike many existing techniques such as DCCA and 2D CCA, SLPCCA aims to preserve the sparse reconstructive relationship of the data, which is achieved by minimizing a regularization-related objective function. The obtained projections contain natural discriminating information even if no class labels are provided. As SLPCCA is a linear method, nonlinear extension is further proposed which can map the input space to a high-dimensional feature space. Experimental results demonstrate the efficiency of the proposed method.