Awesome

Localized Ensemble of Nonnegative Matrix Factorization (L-EnsNMF)

This package includes MATLAB implementations for L-EnsNMF as well as other state-of-the-art topic modeling methods. The methods are as follows.

• Standard NMF
• Sparse NMF
• Orthogonal NMF
• LDA
• L-EnsNMF

Experiment

main.m is a program for running experiment(s) on dataset(s) using the above methods. The program returns topic keywords of each topic modeling method and evaluation results. Some of them are as follows.

• Wtopk - cell array (1 x mcnt) where mcnt is number of methods
• speed - cell array (1 x mcnt) where mcnt is number of methods
• totcvrg_mat - matrix (k x mcnt) where k is number of keywords

Evaluation

This package includes two evaluation measures:

• Topic coherence
• Total document coverage

While one can see how it can be used from main.m, they have separate, simple usage example files, example_pmi.m and example_total_doc_cvrg.m. More details about them can be found here.

References

1. Sangho Suh, Jaegul Choo, Joonseok Lee and Chandan K. Reddy. L-EnsNMF: Boosted Local Topic Discovery via Ensemble of Nonnegative Matrix Factorization. International Conference on Data Mining(ICDM), 2016.

2. D. Kuang and H. Park. Fast rank-2 nonnegative matrix factorization for hierarchical document clustering. In Proc. the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pages 739?747, 2013.

3. Jingu Kim, Yunlong He, and Haesun Park. Algorithms for Nonnegative Matrix and Tensor Factorizations: A Unified View Based on Block Coordinate Descent Framework. Journal of Global Optimization, 58(2), pp. 285-319, 2014.

4. J. Kim and H. Park. Sparse nonnegative matrix factorization for clustering. 2008.

5. D. Newman, J. H. Lau, K. Grieser, and T. Baldwin. Automatic evaluation of topic coherence. In Proc. the Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL HLT), pages 100–108, 2010.

6. H. Kim and H. Park. Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis.

7. H. Kim and H. Park. Nonnegative matrix factorization based on alternating nonnegativity constrained least squares and active set method. SIAM Journal on Matrix Analysis and Applications, 30(2):713?730, 2008.

8. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. Journal of Machine Learning Research (JMLR), 3:993?1022, 2003.

9. https://github.com/kimjingu/nonnegfac-matlab

10. http://www.cc.gatech.edu/~hpark/software/nmf bpas.zip

11. http://davian.korea.ac.kr/myfiles/list/Codes/orthonmf.zip

12. http://psiexp.ss.uci.edu/research/programs data/toolbox.html

Feedback

Please send bug reports, comments, or questions to Sangho Suh. Contributions and extentions with new algorithms are welcome.