LDA enhanced one-bit compressive sensing method for high-throughput mass spectrometry data feature selection

Abstract

Mass spectrometry (MS) is commonly used for the proteins detection of biological samples. However, it generates high-throughput data, posing huge computational challenges for signal processing. In previous studies, we have proposed a one-bit perturbed block CS (OB-PBCS) method to realize feature selection (FS) and obtain a feature set with reduced dimension for classification. However, its feature selection is realized by solving the reconstruction problem with simple greedy strategies, which fails to consider the final classification objective during reconstruction. Considering such problem, in this paper, we propose a hybrid linear discriminant analysis (LDA) enhanced one-bit CS method, called L-OBPBCS, which selects features by considering the subsequent classification and consists of three parts. The first part roughly filters out the features that do not favor the classification task with the proposed single feature decision coefficient. Then, OB-PBCS obtains significant features from the filtered feature set. Afterwards, LDA algorithm is further used for refining, where the amplitude of the optimal projection vector is gathered as the linear discrimination mapping coefficients to identify important features. To show the efficiency of our proposed L-OBPBCS method, classification based on the selected features for both synthesized dataset and real ovarian cancer MS dataset is evaluated. Results show that the feature set derived by L-OBPBCS ensures excellent classification performance, which surpasses the existing one-bit type FS method, and the computational complexity of L-OBPBCS is obviously reduced compared with the existing OB-PBCS method.