Papers by Author: Xiao Hong Su

Abstract: Feature selection is an preprocessing step in pattern analysis and machine learning. In this paper, we design a algorithm for feature subset. We present L1-norm regularization technique for sparse feature weight. Margin loss are introduced to evaluate features, and we employs gradient descent to search the optimal solution to maximize margin. The proposed technique is tested on UCI data sets. Compared with four margin based loss functions for SVM, the proposed technique is effective and efficient.

Abstract: This paper presents a track-before-detect (TBD) algorithm for detection of unknown quantity of targets with parabolic tracks. First, eliminate large number of clutters orderly by expanded trellis and the method of mean power. And then get candidate parabolas by Randomized Hough Transform (RHT). The true tracks of the targets are extracted successfully by a strategy of outliers eliminating at last. Experimental results indicate that the proposed algorithm is capable of detecting multiple targets without the assumptions of an upper bound to the number of targets.