SEMI-SUPERVISED RADIO TRANSMITTER CLASSIFICATION BASED ON ELASTIC SPARSITY REGULARIZED SVM

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.

Elastic Multiple Kernel Learning

(MKL ) 多重核学习被建议处理核熔化。MKL 听说线性联合几个核并且解决同时与联合的核联系的支持的向量机器(SVM ) 。MKL 的当前的框架鼓励核联合系数的稀少。核的重要部分什么时候是增进知识的，强迫稀少，趋于选择仅仅一些核并且可以忽略有用信息。在这份报纸，我们建议学习的有弹性的多重核(EMKL ) 完成适应的核熔化。EMKL 使用混合规则化功能损害稀少和非稀少。MKL 和 SVM 能被认为是 EMKL 的特殊情况。为 MKL 问题基于坡度降下算法，我们建议一个快算法解决 EMKL 问题。模拟数据集上的结果证明 EMKL 的表演有利地比作 MKL 和 SVM。我们进一步把 EMKL 用于基因集合分析并且得到有希望的结果。最后，我们学习比作另外的非稀少的 MKL 的 EMKL 的理论优点。