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
This paper is concerned with the composite row sparsity regularized(c RSR)minimization problem,which captures a number of important applications arising in machine learning,statistics,signal and image processing,and s...This paper is concerned with the composite row sparsity regularized(c RSR)minimization problem,which captures a number of important applications arising in machine learning,statistics,signal and image processing,and so forth.Due to the non-convexity and discontinuity of the composite row sparsity regularization term,the c RSR problem is NP-hard in general.In this paper,we study the optimality conditions of the c RSR problem and derive its stationary equation which is crucial to design efficient algorithms.Based on this stationary equation,an easy-to-implement Newton method is designed to solve the c RSR problem(Nc RSR).The quadratic convergence rate and iteration complexity estimation of Nc RSR are rigorously proved under some mild conditions.Furthermore,Nc RSR is used for solving the regularized clustering and trend filtering problems.Extensive experimental results illustrate that our approach has superior performance compared with the stateof-the-art methods.In particular,Nc RSR not only possesses perfect clustering performance and estimation accuracy but also is faster than all the compared methods.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 Project funded by China Postdoctoral Science Foundation(Grant No.2022M723327)National Natural Science Foundation of China(Grant Nos.12371322 and 12071022)。
文摘This paper is concerned with the composite row sparsity regularized(c RSR)minimization problem,which captures a number of important applications arising in machine learning,statistics,signal and image processing,and so forth.Due to the non-convexity and discontinuity of the composite row sparsity regularization term,the c RSR problem is NP-hard in general.In this paper,we study the optimality conditions of the c RSR problem and derive its stationary equation which is crucial to design efficient algorithms.Based on this stationary equation,an easy-to-implement Newton method is designed to solve the c RSR problem(Nc RSR).The quadratic convergence rate and iteration complexity estimation of Nc RSR are rigorously proved under some mild conditions.Furthermore,Nc RSR is used for solving the regularized clustering and trend filtering problems.Extensive experimental results illustrate that our approach has superior performance compared with the stateof-the-art methods.In particular,Nc RSR not only possesses perfect clustering performance and estimation accuracy but also is faster than all the compared methods.
