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《Journal of Infrared and Millimeter Waves》 2013-03
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Gaussian process latent variable model based on immune clonal selection for SAR target feature extraction and recognition

ZHANG Xiang-Rong,GOU Li-Min,LI Yang-Yang,FENG Jie,JIAO Li-Cheng(Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University,Xi'an 710071,China)  
As a nonlinear dimension reduction algorithm,Gaussian process latent variable model(GPLVM) has been widely applied in pattern recognition and computer vision for its capability in dealing with small size and high-dimensional samples.As GPLVM can discover low-dimensional manifolds in high-dimensional data given only a small number of samples,a new SAR target recognition method was proposed,in which a modified GPLVM was used for feature extraction and Gaussian process classification was employed as the classifier.In GPLVM,the likelihood was optimized by using the scaled conjugate gradient.In order to avoid the noise effect to gradient estimate and overcome the disadvantage that the performance is severely affected by the step length,the immune clone selection algorithm based GPLVM was developed for target feature extraction where the immune clonal selection algorithm characterized by rapid convergence to global optimum was utilized to improve the performance.The experimental results show that the method not only reduces the dimension but also gets higher accuracy.
【Fund】: 国家自然科学基金(61272282 61072106 61203303 61272279);; 陕西省自然科学基金(2011JQ8020);; 中央高校基本科研业务费专项资金资助(JY10000902001 JY10000902045 K50511020011)~~
【CateGory Index】: TN957.52
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