Gene expression data classification based on improved semi-supervised local Fisher discriminant analysis

A new manifold learning method, called improved semi-supervised local fisher discriminant analysis (iSELF), for gene expression data classification is proposed. Motivated by the fact that semi-supervised and parameter-free are two desirable and promising characteristics for dimension reduction, a new difference-based optimization objective function with unlabeled samples has been designed. The proposed method preserves the global structure of unlabeled samples in addition to separating labeled samples in different classes from each other. The semi-supervised method has an analytic form of the globally optimal solution and it can be computed based on Eigen decompositions. Experiments on synthetic data and SRBCT, DLBCL and brain tumor gene expression datasets are performed to test and evaluate the proposed method. The experimental results and comparisons demonstrate the effectiveness of the proposed method.