Abstract
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.
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Hu, H. (2012). Kernel Sparse Locality Preserving Canonical Correlation Analysis for Multi-modal Feature Extraction. In: Zheng, WS., Sun, Z., Wang, Y., Chen, X., Yuen, P.C., Lai, J. (eds) Biometric Recognition. CCBR 2012. Lecture Notes in Computer Science, vol 7701. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35136-5_41
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DOI: https://doi.org/10.1007/978-3-642-35136-5_41
Publisher Name: Springer, Berlin, Heidelberg
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