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[APSIPA ASC 2022] "Robust Online Tucker Dictionary Learning from Multidimensional Data Streams". In Proc. 14th APSIPA Annual Summit and Conference, 2022.

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ROTDL: Robust Online Tucker Dictionary Learning from Multidimensional Data Streams

We propose a novel online algorithm called ROTDL for the problem of robust tensor tracking under the Tucker format. ROTDL is not only capable of tracking the underlying Tucker dictionary of multidimensional data streams over time, but also robust to sparse outliers. The proposed algorithm is specifically designed by using the alternating direction method of multipliers, block-coordinate descent, and recursive least-squares filtering techniques.

Capture

DEMO

  • Run "demo_xyz.m" for synthetic experiments.

State-of-the-art algorithms for comparison

Some Results

  • Effect of the noise level

noise

  • Effect of the time-varying factor

time-varying

  • Effect of outliers

outlier

  • Comparsion

compare

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, T.T. Duy, K. Abed-Meraim, N. L. Trung and A. Hafiane. “Robust Online Tucker Dictionary Learning from Multidimensional Data Streams”. In Proc. 14th APSIPA-ASC, 2022. [PDF].

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[APSIPA ASC 2022] "Robust Online Tucker Dictionary Learning from Multidimensional Data Streams". In Proc. 14th APSIPA Annual Summit and Conference, 2022.

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