Patch alignment for graph embedding

Luo, Y; Tao, D; Xu, C

Patch alignment for graph embedding

Luo, Y Tao, D

Xu, C

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Publication Type:: Chapter
Citation:: Graph Embedding for Pattern Analysis, 2013, pp. 73 - 118
Issue Date:: 2013-01-01

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Field	Value	Language
dc.contributor.author	Luo, Y	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Xu, C	en_US
dc.date.issued	2013-01-01	en_US
dc.identifier.citation	Graph Embedding for Pattern Analysis, 2013, pp. 73 - 118	en_US
dc.identifier.isbn	9781461444565	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28277
dc.description.abstract	© Springer Science+Business Media New York 2013. 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.	en_US
dc.relation.ispartof	Graph Embedding for Pattern Analysis	en_US
dc.relation.isbasedon	10.1007/978-1-4614-4457-2_4	en_US
dc.title	Patch alignment for graph embedding	en_US
dc.type	Chapter
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© Springer Science+Business Media New York 2013. 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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http://hdl.handle.net/10453/28277