Latent smooth skeleton embedding

Wang, L; Mao, Q; Tsang, IW

Latent smooth skeleton embedding

Wang, L Mao, Q Tsang, IW

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Publication Type:: Conference Proceeding
Citation:: 31st AAAI Conference on Artificial Intelligence, AAAI 2017, 2017, pp. 2703 - 2709
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Wang, L	en_US
dc.contributor.author	Mao, Q	en_US
dc.contributor.author	Tsang, IW https://orcid.org/0000-0001-8095-4637	en_US
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	31st AAAI Conference on Artificial Intelligence, AAAI 2017, 2017, pp. 2703 - 2709	en_US
dc.identifier.uri	http://hdl.handle.net/10453/125840
dc.description.abstract	Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Learning a smooth skeleton in a low-dimensional space from noisy data becomes important in computer vision and computational biology. Existing methods assume that the manifold constructed from the data is smooth, but they lack the ability to model skeleton structures from noisy data. To overcome this issue, we propose a novel probabilistic structured learning model to learn the density of latent embedding given high-dimensional data and its neighborhood graph. The embedded points that form a smooth skeleton structure are obtained by maximum a posteriori (MAP) estimation. Our analysis shows that the resulting similarity matrix is sparse and unique, and its associated kernel has eigenvalues that follow a power law distribution, which leads to the embeddings of a smooth skeleton. The model is extended to learn a sparse similarity matrix when the graph structure is unknown. Extensive experiments demonstrate the effectiveness of the proposed methods on various datasets by comparing them with existing methods.	en_US
dc.relation.ispartof	31st AAAI Conference on Artificial Intelligence, AAAI 2017	en_US
dc.title	Latent smooth skeleton embedding	en_US
dc.type	Conference Proceeding
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
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Learning a smooth skeleton in a low-dimensional space from noisy data becomes important in computer vision and computational biology. Existing methods assume that the manifold constructed from the data is smooth, but they lack the ability to model skeleton structures from noisy data. To overcome this issue, we propose a novel probabilistic structured learning model to learn the density of latent embedding given high-dimensional data and its neighborhood graph. The embedded points that form a smooth skeleton structure are obtained by maximum a posteriori (MAP) estimation. Our analysis shows that the resulting similarity matrix is sparse and unique, and its associated kernel has eigenvalues that follow a power law distribution, which leads to the embeddings of a smooth skeleton. The model is extended to learn a sparse similarity matrix when the graph structure is unknown. Extensive experiments demonstrate the effectiveness of the proposed methods on various datasets by comparing them with existing methods.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/125840