Recursive Null Space LDA Based Feature Selection for Protein Mass Spectrometry

Protein mass spectrometry has become a popular tool for cancer diagnosis. Feature selection and classification techniques play an important role in the identification of protein biomarkers. In this paper, based on the protein spectrum of cancer classification, an efficient combination of wavelet features and Recursive Null Space LDA algorithm for feature selection is proposed. Firstly, the multi-resolution wavelet decomposition is used to extract the detail features of the protein spectrum data. Then, in order to reduce the dimension of the features, we use T-test for screening the data sets. Thirdly, the Recursive Null Space LDA algorithm is adopted to screen out the most discriminative protein features. Finally, according to the optimal feature set, we use nearest neighbor classifier to estimate the performance. The experimental results on public ovarian cancer data set OC-WCX2a show the promising performance of the proposed algorithm.