SEMAFORE: SEmantic visualization with MAniFOld REgularization ------------------------------------------------------------- INTRODUCTION This is an implementation of SEMAFORE - a semantic visualization method from Le & Lauw (AAAI 2014, JAIR 2016). Usage: perl semafore.pl --num_topics $num_topics --dim $dim --lambda $lambda --alpha $alpha, --beta $beta, --gamma $gamma, --basis_function $basis_function --EM_iter $EM_iter, --Quasi_iter $Quasi_iter --data $data --graph $graph --output_file $output_file Arguments: $num_topics: number of topics $dim: number of dimensions (default 2) $lambda: regularization parameter (default 10) $alpha: Dirichlet parameter (default 0.01) $beta: covariance for Gaussian prior of topic coordinates (default 0.1*$num_docs) $gamma: covariance for Gaussian prior of document coordinates (default 0.1*$num_topics) $basis_function: 0 for Gaussian, 1 for Student-t (with 1 degree of freedom) (default 0) $EM_iter: number of iterations for EM (default 100) $Quasi_iter: maximum iterations of Quasi-Newton (default 10) $data: input data $graph: neighborhood graph $output_file: output file Details: + This implementation needs Algorithm::LBFGS library for quasi-Newton method L-BFGS. The library can be downloaded at http://search.cpan.org/~laye/Algorithm-LBFGS-0.16/lib/Algorithm/LBFGS.pm. To install, cpan Algorithm::LBFGS + If $graph is not passed, SEMAFORE turns into Probabilistic Latent Semantic Visualization (PLSV) (Iwata et al., 2008) + Example of input data with 3 documents (numbers are ids of words): 0 1 1 2 2 3 4 4 5 6 7 7 8 8 8 8 9 10 11 12 13 13 14 14 15 15 15 16 17 18 19 20 20 21 22 23 24 25 25 25 25 25 25 26 27 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 50 51 52 53 54 54 55 56 57 58 + Neighborhood graph is represented by a matrix A: NxN. N is the number of documents and A[i,j]=A[j,i] is the weight of the edge ij. For example, 0 1 0 1 0 1 0 1 0 HOW TO CITE If you use SEMAFORE for your research, please cite: @article{le16a, title={Semantic Visualization with Neighborhood Graph Regularization}, author={Le, Tuan MV and Lauw, Hady W}, journal={Journal of Artificial Intelligence Research}, volume={55}, pages={1091--1133}, year={2016} } The paper can be downloaded from: http://jair.org/papers/paper4983.html Or @inproceedings{le2014manifold, title={Manifold learning for jointly modeling topic and visualization}, author={Le, Tuan MV and Lauw, Hady W}, booktitle={Proceedings of the AAAI Conference on Artificial Intelligence}, year={2014} } The paper can be downloaded from: http://www.hadylauw.com/publications/aaai14.pdf BIBLIOGRAPHY @inproceedings{iwata2008probabilistic, title={Probabilistic latent semantic visualization: topic model for visualizing documents}, author={Iwata, Tomoharu and Yamada, Takeshi and Ueda, Naonori}, booktitle={Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining}, pages={363--371}, year={2008}, organization={ACM} }