A Unified Subspace Classification Framework Developed for Diagnostic System Using Microwave Signal
Paper i proceeding, 2013

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.


Supervised subspace learning

high dimensional data

class separability


Yinan Yu

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik

Tomas McKelvey

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik

European Signal Processing Conference

22195491 (ISSN)

978-099286260-2 (ISBN)


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Informations- och kommunikationsteknik



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