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Multilevel Analysis of Attributed Graphs for Explicit Graph Embedding in Vector Spaces Read chapter Read first chapter

2013 | OriginalPaper | Chapter

Patch Alignment for Graph Embedding

Authors : Yong Luo, Dacheng Tao, Chao Xu

Published in: Graph Embedding for Pattern Analysis

Publisher: 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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previous chapter Median Graph Computation by Means of Graph Embedding into Vector Spaces
next chapter Improving Classifications Through Graph Embeddings
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Metadata
Title
Patch Alignment for Graph Embedding
Authors
Yong Luo
Dacheng Tao
Chao Xu
Copyright Year
2013
Publisher
Springer New York
Book
Graph Embedding for Pattern Analysis

Print ISBN: 978-1-4614-4456-5

Electronic ISBN: 978-1-4614-4457-2

Copyright Year: 2013

DOI
https://doi.org/10.1007/978-1-4614-4457-2_4