About
This website provides matlab code and some details about the Rate Adapting Poisson Model. The model was published at ICML 2006. You can find the publication here.
The Rate Adapting Poisson (RAP) model is an undirected probabilistic graphical model suited to learn latent structure in count data. It can be used to find dimensionality reduced representations of such data which subsequently can be used for classification or retrieval algorithms. In the ICML paper it is shown that for some benchmark datasets for this task the RAP model generates superior representations for such tasks than its directed counterpart Probabilisitic Latent Semantic Analysis (pLSI). For more details and a description of the algorithm please have a look in the paper
Below you can see a picture showing a 2 dimensional projection of text data
Code
You can download my maltab implementation of the RAP model:
Matlab source for RAP
Just unzip the package and have a look at sampleRun.m to see how to use the code. The function har_learn.m has also an extensive help which might help you. Please note that you need to install Tom Minkas Lightspeed package. Make sure you compile all files inside it!! Besides the implementation of this model I also have other implementations which might be helpful. These can be found on my code website.
| pLSI - probabilistic latent semantic analysis, including a version of the tempered EM algorithm. |
| ePCA - exponential family PCA. |
And of course there is the wonderful spider with implementations of NMF, etc.
Datasets
Text Datasets
For the evaluation of the model we used several benchmark datasets
for information retrieval. All of them were obtained from the
website of Alessandro Moschitti.
For more details and the original sources please have a look at this website.
These corpora were processed using the
Rainbow toolbox
from Andrew McCallum. In the version offered on this website the
switches like stemming, pruning, etc. are ignored without warning.
I replaced the function lex-simple.c with
this version to fix this. You can download the preprocessed matlab data
and the scripts for the generation of this data:
| 20Newsgroups |
count data |
dictionary |
(Scripts used for creation) |
|
Details: 10000 words with highest mutual information with class variable, Porter Stemming, pruned all words with < 3 characters, applied a stopword list |
| Ohsumed (Medline) |
count data |
dictionary |
(Scripts used for creation) |
|
Porter Stemming, pruned all words with < 3 characters, applied a stopword list, pruned words which occured only once, pruned words which occur in only one document |
| Reuters21578 |
count data:train test |
dictionary |
(Scripts used for creation) |
|
Porter Stemming, pruned all words with < 3 characters, applied a stopword list, pruned words which occured only once, pruned words which occur in only one document |
Computer Vision Datasets
References
Contact
pgehler@tuebingen.mpg.de.
