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231224s2012 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2011.154
|2 doi
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|a eng
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| 100 |
1 |
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|a Memisevic, Roland
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Shared Kernel Information Embedding for discriminative inference
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|c 2012
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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| 500 |
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|a Date Completed 10.09.2012
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|a Date Revised 31.05.2012
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|a published: Print
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|a Citation Status MEDLINE
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|a Latent variable models, such as the GPLVM and related methods, help mitigate overfitting when learning from small or moderately sized training sets. Nevertheless, existing methods suffer from several problems: 1) complexity, 2) the lack of explicit mappings to and from the latent space, 3) an inability to cope with multimodality, and 4) the lack of a well-defined density over the latent space. We propose an LVM called the Kernel Information Embedding (KIE) that defines a coherent joint density over the input and a learned latent space. Learning is quadratic, and it works well on small data sets. We also introduce a generalization, the shared KIE (sKIE), that allows us to model multiple input spaces (e.g., image features and poses) using a single, shared latent representation. KIE and sKIE permit missing data during inference and partially labeled data during learning. We show that with data sets too large to learn a coherent global model, one can use the sKIE to learn local online models. We use sKIE for human pose inference
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|a Journal Article
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| 650 |
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|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Sigal, Leonid
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Fleet, David J
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 34(2012), 4 vom: 23. Apr., Seite 778-90
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
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|g volume:34
|g year:2012
|g number:4
|g day:23
|g month:04
|g pages:778-90
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|u http://dx.doi.org/10.1109/TPAMI.2011.154
|3 Volltext
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|d 34
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|h 778-90
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