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提出一种基于局部判别正切空间排列(local discriminative tangent space alignment,LDTSA)的高光谱影像降维方法。LDTSA源于局部正切空间排列(LTSA)中的排列机制,在一个局域块内利用线性局部正切平面对类内样本的流形结构建模,同时还考虑到类间判别信息以最大化判别边界。利用多幅高光谱数据进行降维和分类试验。结果表明,LDTSA主要有三个优点:①在小样本问题上性能稳定;②在降维过程中保持类别间的判别信息;③有效挖掘数据集的几何流形结构。 更多还原

【Abstract】 A local discriminative tangent space alignment(LDTSA) based dimension reduction method is proposed.It applies the idea of part optimization and whole alignment and considers encoding the geometric and discriminative information in a local patch.The experiment demonstrate the effectiveness of LDTSA compared with representative dimensionality reduction algorithms.① LDTSA avoids the small-sample-size problem;② LDTSA preserves the discriminative ability;③ LDTSA has the ability to detect the intrinsi... 更多还原