Worst Separation Spatially Smooth Discriminant Analysis with Constrained Average Compactness
NIU Lu-Lu1, CHEN Song-Can1,2, YU Lu3
1.College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016 2.State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093 3.Institute of Communications Engineering, PLA University of Science and Technology, Nanjing 210007
Abstract：Spatially Smooth Linear Discriminant Analysis(SLDA) and IMage Euclidean Distance Discriminant Analysis(IMEDA)combined with spatial structure information of the imagesare two main discriminant methods to reduce dimension, and the classification performance of SLDA and IMEDA is better than that of LDA. Different from SLDA and IMEDA, the solutions in the proposed algorithms called WSLDA and WIMEDAare obtained by parameterizing projection directions, maintaining an upper bound for average within-class scatter and maximizing the minimal between-class scatter.Also their solution can simply be attributed to solve a well-known eigenvalue optimization problem called minimization for the maximal eigenvalue of a symmetric matrix. It overcomes the shortcoming that many algorithms need to use full eigenvalue decomposition. In addition, experiments on standard face dataset Yale、AR and FERET validate the effectiveness of WSLDA and WIMEDA.
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2014, Vol. 27 
