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Partitioned Tensor Factorization using Orthogonal Procrustes Matching

A Demo using the Fast C++ Implementation

  • 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 ./ in bash):

./gdlm.Platform -j200 -a output/Est_Alpha.csv -o output/Est_Multinomial.csv -r output/Est_Proportions.csv problems/n10000d10000k5eps0.05.conf

The command will create multiple threads to process the partitions whose number is specified by -j.

Matlab Code



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: In the framework, replace the calls to tensor power method with the functions defined here.

Main files

  • ptpqp.m --- implements the proposed PTPQP algorithm
  • tpqp.m --- same as above but without partitioning

Other files

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