Local procrustes for manifold embedding: a measure of embedding quality and embedding algorithms

被引:28
作者
Goldberg, Yair [1 ,2 ]
Ritov, Ya'acov [1 ,2 ]
机构
[1] Hebrew Univ Jerusalem, Dept Stat, IL-91905 Jerusalem, Israel
[2] Hebrew Univ Jerusalem, Ctr Study Rat, IL-91905 Jerusalem, Israel
关键词
Dimension reducing; Manifold learning; Procrustes analysis; Local PCA; Simulated annealing; NONLINEAR DIMENSIONALITY REDUCTION; ROBUSTNESS;
D O I
10.1007/s10994-009-5107-9
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We present the Procrustes measure, a novel measure based on Procrustes rotation that enables quantitative comparison of the output of manifold-based embedding algorithms such as LLE (Roweis and Saul, Science 290(5500), 2323-2326, 2000) and Isomap (Tenenbaum et al., Science 290(5500), 2319-2323, 2000). The measure also serves as a natural tool when choosing dimension-reduction parameters. We also present two novel dimension-reduction techniques that attempt to minimize the suggested measure, and compare the results of these techniques to the results of existing algorithms. Finally, we suggest a simple iterative method that can be used to improve the output of existing algorithms.
引用
收藏
页码:1 / 25
页数:25
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