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一种改进的局部保持投影高光谱特征提取算法
引用本文:屈晓刚,何明一,梅少辉.一种改进的局部保持投影高光谱特征提取算法[J].现代电子技术,2011,34(13):74-77,80.
作者姓名:屈晓刚  何明一  梅少辉
作者单位:西北工业大学陕西省信息获取与处理重点实验室,陕西西安,710129
摘    要:局部保持投影算法仅能保持近邻样本的局部结构,无法保证提取的特征有利于后续分类识别。为此,提出一种半监督保持投影特征提取算法。SPP算法能够利用标记样本所携带的类别信息来约束未标记样本,从而提高样本的可分性;同时,还在目标函数中加入一正则项,避免了因矩阵奇异导致算法无法求解的问题。利用实际高光谱数据进行对比实验,结果表明,用SPP算法进行特征提取后的分类精度较LPP算法有显著提升,验证了它的有效性。

关 键 词:局部保持投影  特征提取  半监督  高光谱

An Improved Algorithm for Hyperspectral Data Feature Extraction in Locality Preserving Projections
QU Xiao-gang,HE Ming-yi,MEI Shao-hui.An Improved Algorithm for Hyperspectral Data Feature Extraction in Locality Preserving Projections[J].Modern Electronic Technique,2011,34(13):74-77,80.
Authors:QU Xiao-gang  HE Ming-yi  MEI Shao-hui
Affiliation:QU Xiao-gang,HE Ming-yi,MEI Shao-hui(Shaanxi Key Lab of Information Acquisition and Processing,Northwestern Polytechnical University,Xi'an 710129,China)
Abstract:Since locality preserving projections(LPP) only preserves the local structure and cannot guarantee the extracted features helpful for classification,a feature extraction algorithm of semi-supervised preserving projections(SPP) is proposed.The proposed method can use the classification information carried by the labeled samples to restrain the unlabeled samples,so as to improve the divisibility of samples.Moreover,the problem of singular matrix is avoided by adding a regularization term to its objective func...
Keywords:locality preserving projection  feature extraction  semi-supervised learning  hyperspectral data  
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