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dc.contributor.authorMa, Xiaolizh_CN
dc.contributor.authorCheng, Wangzh_CN
dc.contributor.authorSun, Zhuozh_CN
dc.contributor.authorWen, Chengluzh_CN
dc.contributor.authorLi, Jonathanzh_CN
dc.contributor.author马小丽zh_CN
dc.contributor.author王程zh_CN
dc.contributor.author孙卓zh_CN
dc.date.accessioned2015-07-22T02:39:28Z
dc.date.available2015-07-22T02:39:28Z
dc.date.issued2013zh_CN
dc.identifier.citationInternational Geoscience and Remote Sensing Symposium (IGARSS), 2013:1434-1437zh_CN
dc.identifier.other20140917388574zh_CN
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/86574
dc.descriptionConference Name:2013 33rd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013. Conference Address: Melbourne, VIC, Australia. Time:July 21, 2013 - July 26, 2013.zh_CN
dc.descriptionThe Institute of Electrical and Electronics Engineers,; Geoscience and Remote Sensing Society (IEEE GRSS)zh_CN
dc.description.abstractWith the introduction of manifold assumption, Laplacian Support Vector Machine (LapSVM) has advantages over the traditional SVM classifiers. However the dual solution of LapSVM is still a major barrier on the further application of LapSVM. Primal optimization is a promising solution to this problem. In this paper, we introduce a novel primal Laplacian Support Vector Machine with Precondition Conjugate Gradient method (PCG) to the problem of hyperspectral images classification which is one type of primal optimization solution. To prove the effectiveness of the proposed method, we apply it into the hyperspectral image data set Indian Pine. The experiment results show higher accuracy and better generalization ability than dual strategy. ? 2013 IEEE.zh_CN
dc.language.isoen_USzh_CN
dc.publisherInstitute of Electrical and Electronics Engineers Inc.zh_CN
dc.source.urihttp://dx.doi.org/10.1109/IGARSS.2013.6723054zh_CN
dc.subjectConjugate gradient methodzh_CN
dc.subjectGeologyzh_CN
dc.subjectIndependent component analysiszh_CN
dc.subjectLaplace transformszh_CN
dc.subjectOptimizationzh_CN
dc.subjectRemote sensingzh_CN
dc.subjectSpectroscopyzh_CN
dc.subjectSupport vector machineszh_CN
dc.titleHyperspectral image classification using Primal Laplacian SVM in preconditioned conjugate gradient solutionzh_CN
dc.typeConferencezh_CN


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