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  The Rate Adapting Poisson Model for Information Retrieval and Object Recognition

Gehler, P., Holub, A., & Welling, M. (2006). The Rate Adapting Poisson Model for Information Retrieval and Object Recognition. In W. Cohen, & A. Moore (Eds.), ICML '06: Proceedings of the 23rd International Conference on Machine Learning (pp. 337-344). New York, NY, USA: ACM Press.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-D13F-5 Version Permalink: http://hdl.handle.net/21.11116/0000-0004-9A7F-1
Genre: Conference Paper

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ICML-2006-Gehler.pdf (Any fulltext), 609KB
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 Creators:
Gehler, PV1, 2, Author              
Holub, AD, Author
Welling, M, Author
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Probabilistic modelling of text data in the bag-of-words representation has been dominated by directed graphical models such as pLSI, LDA, NMF, and discrete PCA. Recently, state of the art performance on visual object recognition has also been reported using variants of these models. We introduce an alternative undirected graphical model suitable for modelling count data. This "Rate Adapting Poisson" (RAP) model is shown to generate superior dimensionally reduced representations for subsequent retrieval or classification. Models are trained using contrastive divergence while inference of latent topical representations is efficiently achieved through a simple matrix multiplication.

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 Dates: 2006-06
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1145/1143844.1143887
BibTex Citekey: 3929
 Degree: -

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Title: 23rd International Conference on Machine Learning (ICML 2006)
Place of Event: Pittsburgh, PA, USA
Start-/End Date: 2006-06-25 - 2006-06-29

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Title: ICML '06: Proceedings of the 23rd International Conference on Machine Learning
Source Genre: Proceedings
 Creator(s):
Cohen, W, Editor
Moore, A, Editor
Affiliations:
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Publ. Info: New York, NY, USA : ACM Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 337 - 344 Identifier: ISBN: 1-59593-383-2