Project details for hca

Logo hca 0.41

by wbuntine - November 29, 2013, 03:16:11 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

view (5 today), download ( 1 today ), 2 subscriptions

Description:

Non-parametric topic models implemented using efficient Gibbs sampling. Early theory from the ECML-PKDD 2011 paper cited.

Coded in C with no other dependencies. No Chinese restaurant processes or stick breaking so fast (non-parametric methods 1-3 times slower than regular LDA with Gibbs, and marginal increase in memory). Input can be LdaC format, docword format, various Matlab style formats. Implements HDP-LDA ala Teh, Jordan Beal and Blei (2006), HPYP-LDA, symmetric-symmetric, symmetric-asymmetric, asymmetric-symmetric, and asymmetric-symmetric priors ala Wallach, Mimno and McCallum (2009) with Pitman-Yor or Dirichlet processes. Burstiness modelling ala Doyle and Elkan (2009) can combine with any model above for even better performance. Full hyper-parameter fitting, or setting initially.

Estimation of various vectors (document and topic vectors). Diagnostics, control, restarts, test likelihood via document completion. Coherence calculations on results using PMI and normalised PMI.

Changes to previous version:

Added example on using burstiness.

BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
URL: Project Homepage
Supported Operating Systems: Linux, Macosx, Windows Under Cygwin
Data Formats: Ascii
Tags: Topic Modeling, Nonparametric Bayes
Archive: download here

Other available revisons

Version Changelog Date
0.41

Added example on using burstiness.

November 29, 2013, 03:16:11
0.4

Added example on using burstiness.

November 25, 2013, 05:15:27

Comments

No one has posted any comments yet. Perhaps you'd like to be the first?

Leave a comment

You must be logged in to post comments.