Supervised locally linear embedding with probability-based distance for classification
Supervised locally linear embedding with probability-based distance for classification
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DOI：
10.1016/j.camwa.2008.10.055
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发表时间：
2009-03-01
影响因子：
2.9
通讯作者：
Zhang, Zhenyue
中科院分区：
文献类型：
--
作者：
Zhao, Lingxiao;Zhang, Zhenyue
提出了一种基于概率距离和局部线性嵌入技术的分类降维方法。采用逻辑判别法（LD）估计概率分布以及对减少的数据进行分类。与仅用于训练数据降维的有监督局部线性嵌入（SLLE）不同，本文提出的基于概率的局部线性嵌入（PLLE）可以同时应用于训练数据和测试数据。五个微阵列数据集在高维空间，IRIS数据，和一组真实的手写数字进行了实验。数值实验结果表明，与应用于LLE或SLLE计算的低维嵌入坐标上的LD分类器相比，该方法的性能更好。(C)2008爱思唯尔有限公司保留所有权利。
We present a novel dimension reduction method for classification based on probabilitybased distance and the technique of locally linear embedding (LLE). Logistic Discrimination (LD) is adopted for estimating the probability distribution as well as for classification on the reduced data. Different from the supervised locally linear embedding (SLLE) that is only used for the dimension reduction of training data, our probability-based locally linear embedding (PLLE) can be applied on both training and testing data. Five microarray data sets in high-dimensional spaces, the IRIS data, and a real set of handwritten digits are experimented. The numerical results show the proposed methodology performs better, compared with the LD classifiers applied on the lower-dimensional embedding coordinates computed by LLE or SLLE. (C) 2008 Elsevier Ltd. All rights reserved.