Non-collaborative radio transmitter recognition is a significant but challenging issue, since it is hard or costly to obtain labeled training data samples. In order to make effective use of the unlabeled samples which...Non-collaborative radio transmitter recognition is a significant but challenging issue, since it is hard or costly to obtain labeled training data samples. In order to make effective use of the unlabeled samples which can be obtained much easier, a novel semi-supervised classification method named Elastic Sparsity Regularized Support Vector Machine (ESRSVM) is proposed for radio transmitter classification. ESRSVM first constructs an elastic-net graph over data samples to capture the robust and natural discriminating information and then incorporate the information into the manifold learning framework by an elastic sparsity regularization term. Experimental results on 10 GMSK modulated Automatic Identification System radios and 15 FM walkie-talkie radios show that ESRSVM achieves obviously better performance than KNN and SVM, which use only labeled samples for classification, and also outperforms semi-supervised classifier LapSVM based on manifold regularization.展开更多
Canonical correlation analysis(CCA)describes the relationship between two sets of variables by finding a linear combination that maximizes the correlation coefficient.However,in high-dimensional settings where the num...Canonical correlation analysis(CCA)describes the relationship between two sets of variables by finding a linear combination that maximizes the correlation coefficient.However,in high-dimensional settings where the number of variables exceeds sample size,or in the case that the variables are highly correlated,the traditional CCA is no longer appropriate.In this paper,a new matrix regularization is introduced,which is an extension of the trace Lasso in the vector case.Then we propose an adaptive sparse version of CCA(ASCCA)to overcome these disadvantages by utilizing the trace Lasso regularization.The adaptability of ASCCA is that the sparsity regularization of canonical vectors depends on the sample data,which is more realistic in practical applications.The ASCCA model is further reformulated to an optimization problem on the Riemannian manifold.Then we adopt a manifold inexact augmented Lagrangian method to solve the resulting optimization problem.The performance of the ASCCA model is compared with some existing sparse CCA techniques in different simulation settings and real datasets.展开更多
基金Supported by the Hi-Tech Research and Development Program of China (No. 2009AAJ130)
文摘Non-collaborative radio transmitter recognition is a significant but challenging issue, since it is hard or costly to obtain labeled training data samples. In order to make effective use of the unlabeled samples which can be obtained much easier, a novel semi-supervised classification method named Elastic Sparsity Regularized Support Vector Machine (ESRSVM) is proposed for radio transmitter classification. ESRSVM first constructs an elastic-net graph over data samples to capture the robust and natural discriminating information and then incorporate the information into the manifold learning framework by an elastic sparsity regularization term. Experimental results on 10 GMSK modulated Automatic Identification System radios and 15 FM walkie-talkie radios show that ESRSVM achieves obviously better performance than KNN and SVM, which use only labeled samples for classification, and also outperforms semi-supervised classifier LapSVM based on manifold regularization.
基金supported by the National Science Foundation of China(No.12071398)the Natural Science Foundation of Hunan Province(No.2020JJ4567)the Key Scientific Research Found of Hunan Education Department(Nos.20A097 and 18A351).
文摘Canonical correlation analysis(CCA)describes the relationship between two sets of variables by finding a linear combination that maximizes the correlation coefficient.However,in high-dimensional settings where the number of variables exceeds sample size,or in the case that the variables are highly correlated,the traditional CCA is no longer appropriate.In this paper,a new matrix regularization is introduced,which is an extension of the trace Lasso in the vector case.Then we propose an adaptive sparse version of CCA(ASCCA)to overcome these disadvantages by utilizing the trace Lasso regularization.The adaptability of ASCCA is that the sparsity regularization of canonical vectors depends on the sample data,which is more realistic in practical applications.The ASCCA model is further reformulated to an optimization problem on the Riemannian manifold.Then we adopt a manifold inexact augmented Lagrangian method to solve the resulting optimization problem.The performance of the ASCCA model is compared with some existing sparse CCA techniques in different simulation settings and real datasets.
