A new nonlinear discriminant analysis algorithm using a combined version of LDA and LLE
نویسندگان
چکیده
Linear discriminant analysis (LDA) is a simple but widely used algorithm in pattern recognition. However it has some shortcomings in that it is sensitive to outliers and limited to linearly separable cases. To solve this problem a new version of nonlinear discriminant algorithm is proposed. This new version, SC-LLE, uses LDA combined with LLE method to take into account non-linearly separable cases. There have been other attempts to solve the problems of LDA including methods using kernels (KPCA). However, we investigate this new concept applied to nonlinear data projection that seems to be promising. Applications on 3D data show the interest of this concept.
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