Partial Membership Latent Dirichlet Allocation
NOTE: If this code is used, cite it: Chao Chen, Alina Zare, & Timotius Andrean Patrick Lagaunne. (2019, April 12). GatorSense/PMLDA v1.0 (Version v1.0). Zenodo. http://doi.org/10.5281/zenodo.2638296
NOTE: If PMLDA used in any publication or presentation, the following reference must be cited:
C. Chen, A. Zare, H. Trinh, G. Omotara, J. T. Cobb, and P. Lagaunne, “Partial Membership Latent Dirichlet Allocation,” IEEE Trans. Image Process., vol. 26, pp. 5590-5602, Dec. 2017.
available: https://arxiv.org/abs/1612.08936
PRIOR TO RUNNING PMLDA:
- PMLDA code uses the following toolboxes and libraries: Lightspeed Matlab toolbox, Eigen Library and GSL (GNU Scientific Library)
- Lightspeed Matlab toolbox (by Tom Minka), which can be downloaded from http://research.microsoft.com/en-us/um/people/minka/software/lightspeed/
- Eigen Library: http://eigen.tuxfamily.org/index.php?title=Main_Page
- GSL Library: https://www.gnu.org/software/gsl/
- The Lightspeed toolbox and the Eigen library must both be downloaded and placed in the support/ folder
- GSL Library must be downloaded and compiled/installed
- After installing the libraries and toolboxes, the functions support/newProbability1.cpp and logmvnpdf1.cpp must be compiled with the following commands:
- mex logmvnpdf1.cpp
- mex newProbability1.cpp
- Add paths to lightspeed, Eigen, and support
The command to run the PMLDA algorithm:
[samples,cluster]=pmlda_gibbs(Data,para)
or
[samples,cluster]=pmlda_leastsquare(Data,para)
pmlda_gibbs conducts both inference stage and cluster estimation stage using Gibbs sampler.
pmlda_leastsquare conducts the inference stage and the cluster estimation stage using a Gibbs sampler and a least squares approach, respectively.
For the above two functions,
Input:
- Data - 1*D cells. Each cell represents a document.
+ Data{i}.X - saves the visual words for document i, which is a N-by-p matrix, N is the number of visual word in document i, and p is the feature dimension. Note that users can add additional fields to Data{i}. This function only uses Data{i}.X - para.topic - scalar, number of topics
- para.alpha - 1-by-para.topic vector, parameter of Dirichlet distribution (prior of topic proportion)
- para.exp - scalar, parameter of exponential distribution (prior of scaling factor)
- para.iter - scalar, number of iterations for Gibbs sampling
Output:
+ samples - save the estimated memberships, topic proportion, scaling factor
+ samples(d).sStar - estimated scaling factor for document d
+ samples(d).piStar - estimated topic proportion for document d
+ samples(d).zStar - estimated membership for document d
+ cluster - save the estimated clusters
+ cluster.mu{k} - estimated mean for cluster k
+ cluster.cov{k} - estimated covariance matrix for cluster k
Simulated data (for one document) can be generated using
function data = genSynData(n,alpha,b,cluster)
Inputs:
+ n - number of data points (words, visual words) to be generated
+ alpha - 1xk vector of dirichlet hypers
+ b - scalar hyper for exponential distribution
+ cluster - cluster parameters
+ cluster{k}.mu - mean of the kth topic
+ cluster{k}.cov - covariance matrix of the kth topic
Outputs:
+ data - a struct
+ data.X - words, n-by-p matrix. p is the feature dimension
+ data.Z - memberships, n-by-k matrix. k is the number of topics
+ data.Scale - scaling factor
+ data.Pi - topic proportion
+ data.cluster - topics (clusters)
Please run demo.m to see how to run PM-LDA and visualize the estimated result.
Files explanation:
Latest Revision: June 2016
- README.md - this file
- demo.m - demo script to run pmlda on synthetic data
- LICENSE - license for the code
- pmlda_gibbs.m - gibbs sampler implementation of pmlda
- pmlda_leastsquare.m - gibbs/least_squares implementation of pmlda
- /support - folder containing helper functions for pmlda implementation
- /support/dirichlet_sample.m - function to generate a sample from dirichlet distribution
- /support/logmvnpdf1.cpp
- /support/newProbability1.cpp
- /support/genSynData.m
For any questions, please contact:
Alina Zare
Email Address:azare@ece.ufl.edu
University of Florida, Department of Electrical and Computer Engineering