Subspace learning is widely used in many signal processing and statistical learning problems where the signal is assumably generated from a low dimensional space. In this paper, we present a unified classifier including several concepts from different subspace techniques, such as PCA, LRC, LDA, GLRT, etc. The objective is to project the original signal (usually of high dimension) into a smaller subspace with 1) within-class data structure preserved and 2) between-class-distance enhanced. A novel classification technique called Maximum Angle Subspace Classifier (MASC) is presented to achieve these purposes. To compensate for the computational complexity and non-convexity of MASC, an approximation is proposed as a trade-off between the classification performance and the computational issue. The approaches are applied to the problem of classifying high dimensional frequency measurements from a microwave based diagnostic system and results are compared with existing methods
