ML Anthology

The Rate Adapting Poisson Model for Information Retrieval and Object Recognition

Peter V. Gehler, Alex Holub, Max Welling
ICML 2006 pp. 337-344
doi:10.1145/1143844.1143887 /icml/2006/gehler2006icml-rate/

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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Text

Gehler et al. "The Rate Adapting Poisson Model for Information Retrieval and Object Recognition." International Conference on Machine Learning, 2006. doi:10.1145/1143844.1143887

Markdown

[Gehler et al. "The Rate Adapting Poisson Model for Information Retrieval and Object Recognition." International Conference on Machine Learning, 2006.](https://mlanthology.org/icml/2006/gehler2006icml-rate/) doi:10.1145/1143844.1143887

BibTeX

@inproceedings{gehler2006icml-rate,
  title     = {{The Rate Adapting Poisson Model for Information Retrieval and Object Recognition}},
  author    = {Gehler, Peter V. and Holub, Alex and Welling, Max},
  booktitle = {International Conference on Machine Learning},
  year      = {2006},
  pages     = {337-344},
  doi       = {10.1145/1143844.1143887},
  url       = {https://mlanthology.org/icml/2006/gehler2006icml-rate/}
}