Feature subset selection is a fundamental problem of data mining. The mutual information of feature subset is a measure for feature subset containing class feature information. A hashing mechanism is proposed to calcu...Feature subset selection is a fundamental problem of data mining. The mutual information of feature subset is a measure for feature subset containing class feature information. A hashing mechanism is proposed to calculate the mutual information of feature subset. The feature relevancy is defined by mutual information. Redundancy-synergy coefficient, a novel redundancy and synergy measure for features to describe the class feature, is defined. In terms of information maximization rule, a bidirectional heuristic feature subset selection method based on mutual information and redundancy-synergy coefficient is presented. This study’s experiments show the good performance of the new method.展开更多
In multi-target tracking,Multiple Hypothesis Tracking (MHT) can effectively solve the data association problem. However,traditional MHT can not make full use of motion information. In this work,we combine MHT with Int...In multi-target tracking,Multiple Hypothesis Tracking (MHT) can effectively solve the data association problem. However,traditional MHT can not make full use of motion information. In this work,we combine MHT with Interactive Multiple Model (IMM) estimator and feature fusion. New algorithm greatly improves the tracking performance due to the fact that IMM estimator provides better estimation and feature information enhances the accuracy of data association. The new algorithm is tested by tracking tropical fish in fish container. Experimental result shows that this algorithm can significantly reduce tracking lost rate and restrain the noises with higher computational effectiveness when compares with traditional MHT.展开更多
文摘Feature subset selection is a fundamental problem of data mining. The mutual information of feature subset is a measure for feature subset containing class feature information. A hashing mechanism is proposed to calculate the mutual information of feature subset. The feature relevancy is defined by mutual information. Redundancy-synergy coefficient, a novel redundancy and synergy measure for features to describe the class feature, is defined. In terms of information maximization rule, a bidirectional heuristic feature subset selection method based on mutual information and redundancy-synergy coefficient is presented. This study’s experiments show the good performance of the new method.
基金Supported by the National Natural Science Foundation of China (No. 60772154)the President Foundation of Graduate University of Chinese Academy of Sciences (No. 085102GN00)
文摘In multi-target tracking,Multiple Hypothesis Tracking (MHT) can effectively solve the data association problem. However,traditional MHT can not make full use of motion information. In this work,we combine MHT with Interactive Multiple Model (IMM) estimator and feature fusion. New algorithm greatly improves the tracking performance due to the fact that IMM estimator provides better estimation and feature information enhances the accuracy of data association. The new algorithm is tested by tracking tropical fish in fish container. Experimental result shows that this algorithm can significantly reduce tracking lost rate and restrain the noises with higher computational effectiveness when compares with traditional MHT.
