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[IEEE ICASSP 2021] "A fast randomized adaptive CP decomposition for streaming tensors". In 46th IEEE International Conference on Acoustics, Speech, & Signal Processing, 2021.

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ROLCP: A fast randomized adaptive CP decomposition for streaming tensors

In this work, we introduce a fast adaptive algorithm for CANDECOMP/PARAFAC decomposition of streaming three-way tensors using randomized sketching techniques. By leveraging randomized least-squares regression and approximating matrix multiplication, we propose an efficient first-order estimator to minimize an exponentially weighted recursive leastsquares cost function. Our algorithm is fast, requiring a low computational complexity and memory storage.

Dependencies

  • Our MATLAB code requires the Tensor Toolbox which is already attached to this repository.
  • MATLAB 2019a

Demo

Quick Start: Just run the file DEMO.m

State-of-the-art algorithms for comparison

Some Results

Running time and estimation accuracy of adaptive CP algorithms

Reference

This code is free and open source for research purposes. If you use this code, please acknowledge the following paper.

[1] L.T. Thanh, K. Abed-Meraim, N.L. Trung, A. Hafiance. "A fast randomized adaptive CP decomposition for streaming tensors". IEEE Int. Conf. Acoust. Speech Signal Process. (IEEE ICASSP), 2021.

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[IEEE ICASSP 2021] "A fast randomized adaptive CP decomposition for streaming tensors". In 46th IEEE International Conference on Acoustics, Speech, & Signal Processing, 2021.

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