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From "Grant Ingersoll (JIRA)" <j...@apache.org>
Subject [jira] Updated: (MAHOUT-123) Implement Latent Dirichlet Allocation
Date Tue, 04 Aug 2009 13:53:14 GMT

     [ https://issues.apache.org/jira/browse/MAHOUT-123?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Grant Ingersoll updated MAHOUT-123:
-----------------------------------

    Attachment: MAHOUT-123.patch

Moved bin/ to examples directory,  Added ASL to some headers.  Ran the test, which seems to
go fine, but it didn't output any topics.

To run, the instructions are now:
{quote}
cd <MAHOUT_HOME>/examples
bin/build-reuters.sh
{quote}

> Implement Latent Dirichlet Allocation
> -------------------------------------
>
>                 Key: MAHOUT-123
>                 URL: https://issues.apache.org/jira/browse/MAHOUT-123
>             Project: Mahout
>          Issue Type: New Feature
>          Components: Clustering
>    Affects Versions: 0.2
>            Reporter: David Hall
>            Assignee: Grant Ingersoll
>             Fix For: 0.2
>
>         Attachments: lda.patch, MAHOUT-123.patch, MAHOUT-123.patch, MAHOUT-123.patch,
MAHOUT-123.patch, MAHOUT-123.patch, MAHOUT-123.patch, MAHOUT-123.patch, MAHOUT-123.patch,
MAHOUT-123.patch
>
>   Original Estimate: 504h
>  Remaining Estimate: 504h
>
> (For GSoC)
> Abstract:
> Latent Dirichlet Allocation (Blei et al, 2003) is a powerful learning
> algorithm for automatically and jointly clustering words into "topics"
> and documents into mixtures of topics, and it has been successfully
> applied to model change in scientific fields over time (Griffiths and
> Steyver, 2004; Hall, et al. 2008). In this project, I propose to
> implement a distributed variant of Latent Dirichlet Allocation using
> MapReduce, and, time permitting, to investigate extensions of LDA and
> possibly more efficient algorithms for distributed inference.
> Detailed Description:
> A topic model is, roughly, a hierarchical Bayesian model that
> associates with each document a probability distribution over
> "topics", which are in turn distributions over words. For instance, a
> topic in a collection of newswire might include words about "sports",
> such as "baseball", "home run", "player", and a document about steroid
> use in baseball might include "sports", "drugs", and "politics". Note
> that the labels "sports", "drugs", and "politics", are post-hoc labels
> assigned by a human, and that the algorithm itself only assigns
> associate words with probabilities. The task of parameter estimation
> in these models is to learn both what these topics are, and which
> documents employ them in what proportions.
> One of the promises of unsupervised learning algorithms like Latent
> Dirichlet Allocation (LDA; Blei et al, 2003) is the ability to take a
> massive collections of documents and condense them down into a
> collection of easily understandable topics. However, all available
> open source implementations of LDA and related topics models are not
> distributed, which hampers their utility. This project seeks to
> correct this shortcoming.
> In the literature, there have been several proposals for paralellzing
> LDA. Newman, et al (2007) proposed to create an "approximate" LDA in
> which each processors gets its own subset of the documents to run
> Gibbs sampling over. However, Gibbs sampling is slow and stochastic by
> its very nature, which is not advantageous for repeated runs. Instead,
> I propose to follow Nallapati, et al. (2007) and use a variational
> approximation that is fast and non-random.
> References:
> David M. Blei, J McAuliffe. Supervised Topic Models. NIPS, 2007.
> David M. Blei , Andrew Y. Ng , Michael I. Jordan, Latent dirichlet
> allocation, The Journal of Machine Learning Research, 3, p.993-1022,
> 3/1/2003
> T. L. Griffiths and M. Steyvers. Finding scientiļ¬c topics. Proc Natl
> Acad Sci U S A, 101 Suppl 1: 5228-5235, April 2004.
> David LW Hall, Daniel Jurafsky, and Christopher D. Manning. Studying
> the History of Ideas Using Topic Models. EMNLP, Honolulu, 2008.
> Ramesh Nallapati, William Cohen, John Lafferty, Parallelized
> variational EM for Latent Dirichlet Allocation: An experimental
> evaluation of speed and scalability, ICDM workshop on high performance
> data mining, 2007.
> Newman, D., Asuncion, A., Smyth, P., & Welling, M. Distributed
> Inference for Latent Dirichlet Allocation. NIPS, 2007.
> Xuerui Wang , Andrew McCallum, Topics over time: a non-Markov
> continuous-time model of topical trends. KDD, 2006
> Wolfe, J., Haghighi, A, and Klein, D. Fully distributed EM for very
> large datasets. ICML, 2008.

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