Classification of handwritten digits using supervised locally linear embedding algorithm and support vector machine
Classification of handwritten digits using supervised locally linear embedding algorithm and support vector machine
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DOI：
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发表时间：
2003
影响因子：
2.2
通讯作者：
Olga Kouropteva;O. Okun;M. Pietikäinen
中科院分区：
文献类型：
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作者：
Olga Kouropteva;O. Okun;M. Pietikäinen
局部线性嵌入（LLE）算法是最近提出的一种用于非线性降维的无监督技术.在本文中，我们描述了它的监督变体（SLLE）。这是一种概念上的新方法，其中类成员信息用于将重叠的高维数据映射到嵌入空间中不相交的聚类中。在实验中，我们结合它与支持向量机（SVM）的分类手写数字从MNIST数据库。
The locally linear embedding (LLE) algorithm is an un- supervised technique recently proposed for nonlinear dimensionality re- duction. In this paper, we describe its supervised variant (SLLE). This is a conceptually new method, where class membership information is used to map overlapping high dimensional data into disjoint clusters in the embedded space. In experiments, we combined it with support vec- tor machine (SVM) for classifying handwritten digits from the MNIST database.