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ICASSP 2020 presentation slide of 'EXTRAPOLATED ALTERNATING ALGORITHMS FOR APPROXIMATE CANONICAL POLYADIC DECOMPOSITION'
- Citation Author(s):
- Submitted by:
- Man Shun Ang
- Last updated:
- 13 May 2020 - 5:42pm
- Document Type:
- Presentation Slides
- Document Year:
- 2020
- Event:
- Presenters:
- EXTRAPOLATED ALTERNATING ALGORITHMS FOR APPROXIMATE CANONICAL POLYADIC DECOMPOSITION
- Paper Code:
- WE3.L6.1
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Tensor decompositions have become a central tool in machine learning to extract interpretable patterns from multiway arrays of data. However, computing the approximate Canonical Polyadic Decomposition (aCPD), one of the most important tensor decomposition model, remains a challenge. In this work, we propose several algorithms based on extrapolation that improve over existing alternating methods for aCPD. We show on several simulated and real data sets that carefully designed extrapolation can significantly improve the convergence speed hence reduce the computational time, especially in difficult scenarios.