The Rate Adapting Poisson Model for Information Retrieval and Object Recognition
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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Text Reference
P. V. Gehler, A. D. Holub, and Max Welling. The rate adapting poisson model for information retrieval and object recognition. In Proceedings of the 23rd International Conference on Machine Learning (ICML 2006), 337–344. New York, NY, USA, 06 2006. ACM Press. URL: http://homepage.kyb.local/bs/people/pgehler/rap/index.html.BibTeX Reference
@inproceedings{GehlerHW_ICML_2006,author = "Gehler, P. V. and Holub, A. D. and Welling, Max",
TAG = "object_recognition",
title = "The Rate Adapting Poisson Model for Information Retrieval and Object Recognition",
year = "2006",
publisher = "ACM Press",
pages = "337-344",
month = "06",
booktitle = "Proceedings of the 23rd International Conference on Machine Learning (ICML 2006)",
address = "New York, NY, USA",
location = "23rd International Conference on Machine Learning",
URL = "http://homepage.kyb.local/bs/people/pgehler/rap/index.html"
}