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Path-based similarity with instance-level constraints for SemiBoost

In this paper, a novel classification method path-based similarity with instance-level constrains for SemiBoost, PBS-SB in short is proposed, and we exploit it for synthetic aperture radar automatic target recognition (SAR-ATR). Different from traditional SemiBoost method that uses the Gaussian kernel similarity, PBS-SB utilizes the path-based similarity, which considers the global consistence of data clusters. Besides, the instance-level constraints are integrated into the similarity measurement to construct the semi-supervised similarity, which provides the local consistence information. The experiments on 5 different data sets and MSTAR (Moving and Stationary Target Acquisition and Recognition) database demonstrate that the proposed method has superior classification performance with respect to competitive methods.