S3CMTF: Fast, accurate, and scalable method for incomplete coupled matrix-tensor factorization

How can we extract hidden relations from a tensor and a matrix data simultaneously in a fast, accurate, and scalable way? Coupled matrix-tensor factorization (CMTF) is an important tool for this purpose. Designing an accurate and efficient CMTF method has become more crucial as the size and dimensio...

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Bibliographic Details
Published in:PloS one 2019, Vol.14 (6), p.e0217316-e0217316
Main Authors: Choi, Dongjin, Jang, Jun-Gi, Kang, U
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Computer and Information Sciences
Computer science
Data mining
Datasets
Decomposition
Engineering
Engineering and Technology
Factorization
Information processing
International conferences
Locking
Mathematical analysis
Methods
Models, Theoretical
Optimization techniques
Parallel processing
Physical Sciences
Research and Analysis Methods
Social networks
Social Sciences
Sparsity
Tensors
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Summary:How can we extract hidden relations from a tensor and a matrix data simultaneously in a fast, accurate, and scalable way? Coupled matrix-tensor factorization (CMTF) is an important tool for this purpose. Designing an accurate and efficient CMTF method has become more crucial as the size and dimension of real-world data are growing explosively. However, existing methods for CMTF suffer from lack of accuracy, slow running time, and limited scalability. In this paper, we propose S3CMTF, a fast, accurate, and scalable CMTF method. In contrast to previous methods which do not handle large sparse tensors and are not parallelizable, S3CMTF provides parallel sparse CMTF by carefully deriving gradient update rules. S3CMTF asynchronously updates partial gradients without expensive locking. We show that our method is guaranteed to converge to a quality solution theoretically and empirically. S3CMTF further boosts the performance by carefully storing intermediate computation and reusing them. We theoretically and empirically show that S3CMTF is the fastest, outperforming existing methods. Experimental results show that S3CMTF is up to 930× faster than existing methods while providing the best accuracy. S3CMTF shows linear scalability on the number of data entries and the number of cores. In addition, we apply S3CMTF to Yelp rating tensor data coupled with 3 additional matrices to discover interesting patterns.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0217316