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A joint feature selection and classifier design is proposed in this paper. The approach adopts the feature dimension extension by power transformation as a new kernel function, which can not only make full use of the input samples to form the nonlinear classification boundary, but also realize the nonlinear feature selection. A zero-mean Gaussian prior with Gamma precision is used to promote sparsity in utilization of features in our model. The experiments based on a measured radar data set demonstrate the practicability and effectiveness of the proposed method.
Radar (Radar), 2011 IEEE CIE International Conference on (Volume:1 )
Date of Conference: 24-27 Oct. 2011