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2013 | OriginalPaper | Buchkapitel

Patch Alignment for Graph Embedding

verfasst von : Yong Luo, Dacheng Tao, Chao Xu

Erschienen in: Graph Embedding for Pattern Analysis

Verlag: Springer New York

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Abstract

Dozens of manifold learning-based dimensionality reduction algorithms have been proposed in the literature. The most representative ones are locally linear embedding (LLE) [65], ISOMAP [76], Laplacian eigenmaps (LE) [4], Hessian eigenmaps (HLLE) [20], and local tangent space alignment (LTSA) [102]. LLE uses linear coefficients, which reconstruct a given example by its neighbors, to represent the local geometry, and then seeks a low-dimensional embedding, in which these coefficients are still suitable for reconstruction. ISOMAP preserves global geodesic distances of all the pairs of examples.

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Metadaten
Titel
Patch Alignment for Graph Embedding
verfasst von
Yong Luo
Dacheng Tao
Chao Xu
Copyright-Jahr
2013
Verlag
Springer New York
DOI
https://doi.org/10.1007/978-1-4614-4457-2_4

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