dc.citation.conferencePlace |
US |
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dc.citation.conferencePlace |
Boston |
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dc.citation.endPage |
5481 |
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dc.citation.startPage |
5473 |
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dc.citation.title |
IEEE Conference on Computer Vision and Pattern Recognition |
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dc.contributor.author |
Kim, Kwang In |
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dc.contributor.author |
Tompkin, James |
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dc.contributor.author |
Pfister, Hanspeter |
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dc.contributor.author |
Theobalt, Christian |
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dc.date.accessioned |
2023-12-19T22:11:38Z |
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dc.date.available |
2023-12-19T22:11:38Z |
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dc.date.created |
2019-02-28 |
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dc.date.issued |
2015-06-07 |
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dc.description.abstract |
The common graph Laplacian regularizer is well-established in semi-supervised learning and spectral dimensionality reduction. However, as a first-order regularizer, it can lead to degenerate functions in high-dimensional manifolds. The iterated graph Laplacian enables high-order regularization, but it has a high computational complexity and so cannot be applied to large problems. We introduce a new regularizer which is globally high order and so does not suffer from the degeneracy of the graph Laplacian regularizer, but is also sparse for efficient computation in semi-supervised learning applications. We reduce computational complexity by building a local first-order approximation of the manifold as a surrogate geometry, and construct our high-order regularizer based on local derivative evaluations therein. Experiments on human body shape and pose analysis demonstrate the effectiveness and efficiency of our method. © 2015 IEEE. |
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dc.identifier.bibliographicCitation |
IEEE Conference on Computer Vision and Pattern Recognition, pp.5473 - 5481 |
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dc.identifier.doi |
10.1109/CVPR.2015.7299186 |
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dc.identifier.issn |
1063-6919 |
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dc.identifier.scopusid |
2-s2.0-84959215246 |
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dc.identifier.uri |
https://scholarworks.unist.ac.kr/handle/201301/32622 |
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dc.identifier.url |
https://ieeexplore.ieee.org/document/7299186 |
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dc.language |
영어 |
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dc.publisher |
IEEE Computer Society |
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dc.title |
Local high-order regularization on data manifolds |
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dc.type |
Conference Paper |
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dc.date.conferenceDate |
2015-06-07 |
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