Intelligent and scalable algorithms for canonical polyadic decomposition
Authors
Aggour, Kareem Sherif
ORCID
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Other Contributors
Yener, Bülent, 1959-
Gittens, Alex
Carothers, Christopher D.
Subramaniyan, Arun K.
Gittens, Alex
Carothers, Christopher D.
Subramaniyan, Arun K.
Issue Date
2019-05
Keywords
Computer science
Degree
PhD
Terms of Use
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
Full Citation
Abstract
We next developed two novel algorithms that employ online learning-based approaches to dynamically select the sketching rate and regularization parameters at runtime, further optimizing CP decompositions while simultaneously eliminating the burden of manual hyperparameter selection. This work is the first to intelligently choose the sketching rate and regularization parameters at each iteration of a CPD algorithm to balance the trade-off between minimizing the runtime and maximizing the decomposition accuracy. On both synthetic and real data, it was observed that for noisy tensors, our intelligent CPD algorithm produces decompositions of accuracy comparable to the exact distributed CPD-ALS algorithm in less time, often half the time. For ill-conditioned tensors, given the same time budget, the intelligent CPD algorithm produces decompositions with significantly lower relative error, often yielding an order of magnitude improvement.
Description
May 2019
School of Science
School of Science
Department
Dept. of Computer Science
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection
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