Project details for hca

Logo hca 0.4

by wbuntine - November 25, 2013, 05:15:27 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

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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. Input can be LdaC format, docword format, various Matlab style formats. Implements HDP-LDA, HPYP-LDA, symmetric-symmetric, symmetric-asymmetric, asymmetric-symmetric, and asymmetric-symmetric priors with Pitman-Yor or Dirichlet processes. Full hyper-parameter fitting, or setting initially. Special "turbo boost" function for even better performance. No Chinese restaurant processes so quite fast (non-parametric methods 1.5-3.0 times slower than regular LDA with Gibbs). 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
Supported Operating Systems: Agnostic
Data Formats: Ascii
Tags: Topic Modeling, Nonparametric Bayes
Archive: download here


Wray Buntine (on June 24, 2014, 06:21:54)
Noticed in this update hyper-parameter fitting of "beta" when using -B doesn't update the parameter. I'll have a new version out shortly along with a few other improvements to fix this.
Wray Buntine (on June 24, 2014, 06:29:59)
Get more details about the theory from the [KDD 2014 paper]( "Experiments with Non-parametric Topic Models"). Will be presenting in New York!
Wray Buntine (on August 22, 2014, 23:19:31)
Tip for the speed freaks - diminishing returns after 10-16 cores due to memory thrashing. We keep it to 8 cores. Also, am carefully studying Aaron Li's brilliant KDD 2014 paper to see about transferring his speedups into hca.

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