Graph attribute embedding via Riemannian submersion learning
Date
2011
Authors
Zhao, Haifeng
Robles-Kelly, Antonio
Zhou, Jun
Lu, Jianfeng
Yang, Jing-Yu
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Academic Press
Abstract
In this paper, we tackle the problem of embedding a set of relational structures into a metric space for purposes of matching and categorisation. To this end, we view the problem from a Riemannian perspective and make use of the concepts of charts on the
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Keywords: Data sets; Digit classification; Embedded graphs; Graph embeddings; Graph matchings; L2-norm; Metric spaces; MPEG-7 database; Node coordinates; Posterior probability; Probability density estimation; Relational matching; Relational structures; Riemannian g Graph embedding; Relational matching; Riemannian geometry
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Source
Computer Vision and Image Understanding
Type
Journal article
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2037-12-31
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