- Provides Matlab code for the paper: http://proceedings.mlr.press/v70/tan17a/tan17a.pdf
- Also includes fast precompiled C++ binaries for Linux and MacOS
- The demo shows how to estimate both the multinomial parameters for each topic as well as the topic proportions of each observation.
- Dataset: data/n10000d10000k5eps0.05.csv contains 10,000 observations each has 10,000 variables. Each variable takes values {0,1,2} and 5% noise is also added (e.g., draw from a uniform distribution with probability 0.05). The ground truth parameters used to generate the data are also given under data/n10000d10000k5eps0.05_*.csv.
- To use other datasets, make sure each variable takes values in {0,...,M_i}, where M_i can vary across variables.
To run the demo, decompress the data and use the following command (or simply ./run_example.sh in bash):
./gdlm.Platform -j200 -a output/Est_Alpha.csv -o output/Est_Multinomial.csv -r output/Est_Proportions.csv problems/n10000d10000k5eps0.05.confThe command will create multiple threads to process the partitions whose number is specified by -j.
- The Matlab code requires Tensor Toolbox 2.6 in the MATLAB default PATH. Available at: http://www.sandia.gov/~tgkolda/TensorToolbox/index-2.6.html
To run the comparison on synthetic data: In Matlab, run the script run_comparison.m. You may need to modify the configurations in the scrirpt. The result will be saved in data/result.txt. NOTE: the other tensor methods take time to run. Consider running this script on multiple machines with different configurations.
To run the crowdsourcing experiment, download the datasets and framework at: https://github.com/zhangyuc/SpectralMethodsMeetEM. In the framework, replace the calls to tensor power method with the functions defined here.
- ptpqp.m --- implements the proposed PTPQP algorithm
- tpqp.m --- same as above but without partitioning
- no_tenfact.m --- implements nojd0 and nojd1, obtained from https://github.com/kuleshov/tensor-factorization
- qrj1d.m --- same as above
- jacobi.m --- same as above
- tpm.m --- implements the tensor power method, obtained from https://github.com/kuleshov/tensor-factorization
- nonnegfac-matlab --- implements Hals, obtained from https://gist.github.com/panisson/7719245
- MELD --- implements MELD, obtained from https://github.com/judyboon/MELD