Online classification for SAR target recognition based on SVM and approximate convex hull vertices selection

Shuguang Ding,Xiangli Nie,Hong Qiao,Bo Zhang

Intelligent Control and Automation（2014）

引用 1|浏览45

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摘要

In this paper, we propose a novel online learning classification algorithm, which is based on support vector machine (SVM) and approximate convex hull vertices selection. Considering the geometrical features of SVM, we can safely delete the samples inside the convex hulls. However, in general, if the dimension of the training data is high, most of the samples are the convex hull vertices and therefore only a few samples can be deleted. To solve this problem, we adopt two steps: (1) reducing the dimension by principal component analysis (PCA); (2) finding an approximate convex hull, which is the main contribution of this paper. The effectiveness of the proposed algorithm is demonstrated through several experiments on synthetic data and MSTAR data sets.

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关键词

computational geometry,image classification,learning (artificial intelligence),object recognition,support vector machines,synthetic aperture radar,mstar data sets,sar target recognition,svm geometrical features,approximate convex hull vertices selection,dimension reduction,online classification,online learning classification algorithm,support vector machine,synthetic data,convex hull,algorithm design and analysis,approximation algorithms,training data

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