Cancer gene expression data is a typical data with high dimension and small sample

identifying it directly will encounter the curse of dimensionality

so needs dimensions reduction.This paper proposes a kind of classification approach based on Spectral Regression (SR)analysis and Kernel space K-Nearest Neighbor(KKNN) classifier for gene expression data

it gets the projection matrix through Spectral Regression Analysis witch can extract effectively discriminative characteristics of low dimensions

and reduces the dimensionality of gene expression data by projection matrix

then identifies the low-dimensional data reduced with the Kernel Space K-Nearest Neighbor Classifier.As the experiments operated on the cancer datasets Prostate

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Tumor and 4

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Tumors demonstrate the effectiveness of the proposed algor

ithm;simultaneously

compared with the K-Nearest Neighbor(KNN) classification approach

The Kernel space K-Nearest Neighbor has a better classification result.