Published 2006
| Published
Book Section - Chapter
Open
The rate adapting poisson model for information retrieval and object recognition
- Creators
- Gehler, Peter V.
- Holub, Alex D.
- Welling, Max
- Others:
- Cohen, William
- Moore, Andrew
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.
Additional Information
Copyright 2006 by the author(s)/owner(s). This material is based upon work supported by the National Science Foundation under Grant No. 0447903.Attached Files
Published - p337-gehler.pdf
Files
p337-gehler.pdf
Files
(295.3 kB)
Name | Size | Download all |
---|---|---|
md5:b9fc2cf3931ed13eb5453be6269b3554
|
295.3 kB | Preview Download |
Additional details
- Eprint ID
- 71515
- Resolver ID
- CaltechAUTHORS:20161026-160525724
- IIS-0447903
- NSF
- Created
-
2016-10-27Created from EPrint's datestamp field
- Updated
-
2021-11-11Created from EPrint's last_modified field