Abstract A feature extraction method is proposed, namely class-wise non-locality preserving projection (CNLPP). The kernelized counterpart of CNLPP linear feature extractor is also established. Based on the linear feature extractor-non-locality preserving projection (NLPP), CNLPP utilizes between-class information to guide the procedure of feature extraction. CNLPP takes both the relation information and the class information into account. A kernel version of CNLPP, namely Kernel based CNLPP (KCNLPP), is developed by applying the kernel trick to CNLPP to enhance its performance on nonlinear feature extraction. Experiments on yeast gene expression data and NCI gene expression data are performed to test and evaluate the performance of the proposed algorithm, and the results show that KCNLPP achieves relatively high recognition accuracy.
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2009, Vol. 22 
