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# huanzhang12 / sampling_tensor_decomp

Fast Approximate Orthogonal Tensor Decomposition Based on Importance Sampling

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# Sublinear Time Tensor Decomposition with Importance Sampling

This repository contains experiment code for our paper, "Sublinear Time Orthogonal Tensor Decomposition", by Zhao Song, David P. Woodruff and Huan Zhang. In our paper, we developed a sublinear algorithm for decomposing large tensors using robust tensor power method, where the computationally expensive tensor contractions are done by importance sampling based on the magnitude of the power iterate variable.

In our experiments we only implemented 3-rd order tensors, and sampling with or without pre-scanning variants of our algorithm. Most parts of the code is based on , and we only add new importance sampling based tensor contraction functions.

## Build

We require the following environment to build the code:

• libfftw3 (provided by package libfftw3-dev on Debian based systems)
• Matlab 2014b or later
• GNU Compiler Collection (GCC) 4.9 or newer versions recommended
• Tested on Ubuntu 16.04, should be able to run other Linux distributions or OSX

You need to modify Makefile to make sure Matlab includes and libraries folders match your Matlab installation. They are defined as variable MAT_INC and MAT_LIB:

MAT_INC = /usr/local/MATLAB/R2014b/extern/include
MAT_LIB = /usr/local/MATLAB/R2014b/bin/glnxa64



To build the program, simply run make. Two binaries, fftspec and fft-lda will be built. We only need fftspec for experiments.

## Datasets

Synthetic dense tensors with different eigengaps and noises can be generated via fftspec. The following command generates a rank-100, dimension 600*600*600 tensor, with sigma=0.01 noise added. Generated tensors will be stored under the folder "data".

./fftspec synth_lowrank 600 100 0.01 1


The last parameter 1 indicates that the 100 eigenvalues decay as $\lambda_i = 1/i$. If you set it to 2, then $\lambda_i = 1/i^2$. If you set it to 3,then $\lambda_i = 1 - (i-1)/k$, where k is the rank.

For the tensors from LDA (Latent Dirichlet Allocation), we provide preprocessed tensors (lzma compressed) in folder LDA_tensor_200 for all 6 datasets we used.

## Run Tensor Decomposition

4 algorithms were provided for tensor decomposition:

• Naive robust tensor power method, computing exact tensor contractions
• Sketching based tensor power method, based on 
• Importance Sampling based tensor power method, without pre-scanning
• Importance Sampling based tensor power method, with pre-scanning

Importance Sampling with pre-scanning provides best theoretical bound where only O(n^2) samples are needed, but it needs to scan the entire tensor first. In practice, the without pre-scanning version (assuming the tensor has bounded slice norms, see our paper for details) works better.

In sketching or sampling based algorithms, the following parameters are needed:

• T: Number of power iterations
• L: The number of starting vectors of the robust tensor power method
• B: The number of sketches used in sketching, or the number of repetitions of sampling
• b: The size of the sketch, or the number of indices sampled

Note that there are small notation differences for the letter b between the code and paper: for the sketching based method, the actual sketch length used is 2^b; and for importance sampling based method, the actual total number of samples is O(nb).

The following command shows how to run tensor decomposition with fftspec:

./fftspec algorithm input rank L T B b output


rank is the number of eigenpairs to be recoveried, algorithm can be slow_rbp, fast_rbp, fast_sample_rbp and prescan_sample_rbp, which corresponds to the 4 algorithms metioned above, respectively. For naive tensor power method slow_rbp, parameter B and b are not needed.

The following example shows how to generate a synthetic tensor, and run different methods to compare their running time and recovery accuracy:

# generate a 800x800x800, rank-100 tensor (takes a while...)
./fftspec synth_lowrank 800 100 0.01 2
# Run sketching based tensor power method
./fftspec fast_rbp data/tensor_dim_800_rank_100_noise_0.01_decaymethod_2.dat 1 50 30 30 16 output_dim_800_rank_100_noise_0.01_decaymethod_2_fastrbp_50_30_30_16_rank1.dat
# Run sampling based tensor power method, without prescanning
./fftspec fast_sample_rbp data/tensor_dim_800_rank_100_noise_0.01_decaymethod_2.dat 1 50 30 30 10 output_dim_800_rank_100_noise_0.01_decaymethod_2_samplerbp_50_30_30_10_rank1.dat
# Run sampling based tensor power method, with prescanning
./fftspec prescan_sample_rbp data/tensor_dim_800_rank_100_noise_0.01_decaymethod_2.dat 1 50 30 30 10 output_dim_800_rank_100_noise_0.01_decaymethod_2_presamplerbp_50_30_30_10_rank1.dat
# Run naive tensor power method (SLOW!)
./fftspec slow_rbp data/tensor_dim_800_rank_100_noise_0.01_decaymethod_2.dat 1 50 30 output_dim_800_rank_100_noise_0.01_decaymethod_2_slowrbp_50_30_rank1.dat
#


We want the residual norm to be small, while keeping the reported CPU time as short as possible. Fixing T and L, you can try different B and b and see how running time and residual change. Generally, using a smaller B and b makes the algorithm run faster, but the residual is likely to increase and divergence may occur when B or b is too small.

For the example above here is the expected outputs. If you got numbers quite different from these you probably hit a bug.

• For sketching based tensor power method:
# [STAT]: prep_cpu_time=12.654545
# [STAT]: prep_wall_time=12.654588
# [STAT]: cpu_time=69.057999
# [STAT]: wall_time=69.058114
# [STAT]: residue=0.089962
# [STAT]: fnorm=1.010029

• For importance sampling based tensor power method (without pre-scanning, no preprocessing time):
# [STAT]: cpu_time=9.078225
# [STAT]: wall_time=9.078256
# [STAT]: residue=0.086472
# [STAT]: fnorm=1.010029


• For importance sampling based tensor power method (with pre-scanning):
# [STAT]: prep_cpu_time=0.659961
# [STAT]: prep_wall_time=0.659971
# [STAT]: cpu_time=10.123327
# [STAT]: wall_time=10.123355
# [STAT]: residue=0.086454
# [STAT]: fnorm=1.010029


• For naive tensor power method:
Too long, don't run!


## Results

We have included a large range of results in the results folder, for both sketching and importance sampling based parameters with different B and b, and different eigenvalue decay rates.

## Contact information

If your have any questions or comments, please open an issue on Github, or send an email to ecezhang@ucdavis.edu. We appreciate your feedback.

## References

 Yining Wang, Hsiao-Yu Tung, Alex J Smola, and Anima Anandkumar. Fast and guaranteed tensor decomposition via sketching. In NIPS, pages 991-999, 2015.

Fast Approximate Orthogonal Tensor Decomposition Based on Importance Sampling

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