Global Versus Local Methods in Nonlinear Dimensionality Reduction
Global Versus Local Methods in Nonlinear Dimensionality Reduction
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DOI：
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发表时间：
2002
影响因子：
0.7
通讯作者：
V. Silva;J. Tenenbaum
中科院分区：
文献类型：
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作者：
V. Silva;J. Tenenbaum
最近提出的非线性降维算法大致分为两类，它们各有优缺点：全局的(ISOMAP[1])和局部的(局部线性嵌入[2]、拉普拉斯特征映射[3])。我们提出了ISOMAP的两个变种，它们结合了全局方法的优点和以前局部方法的独有优势：计算稀疏性和逆共形映射的能力。
Recently proposed algorithms for nonlinear dimensionality reduction fall broadly into two categories which have different advantages and disadvantages: global (Isomap [1]), and local (Locally Linear Embedding [2], Laplacian Eigenmaps [3]). We present two variants of Isomap which combine the advantages of the global approach with what have previously been exclusive advantages of local methods: computational sparsity and the ability to invert conformal maps.