Intelligent and scalable algorithms for canonical polyadic decomposition
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Authors
Aggour, Kareem Sherif
Issue Date
2019-05
Type
Electronic thesis
Thesis
Thesis
Language
ENG
Keywords
Computer science
Alternative Title
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
Full Citation
Publisher
Rensselaer Polytechnic Institute, Troy, NY