ALO-NMF: Accelerated Locality-Optimized Non-negative Matrix Factorization-Reference-Cited by-同舟云学术

ALO-NMF: Accelerated Locality-Optimized Non-negative Matrix Factorization

Published:2020-07-06 Issue: Volume: Page:
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Container-title:Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
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Author:

Moon Gordon E.¹,Ellis J. Austin¹,Sukumaran-Rajam Aravind²,Parthasarathy Srinivasan³,Sadayappan P.⁴

Affiliation:

1. Sandia National Laboratories, Albuquerque, NM, USA

2. Washington State University, Pullman, WA, USA

3. The Ohio State University, Columbus, OH, USA

4. University of Utah, Salt Lake City, UT, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3394486.3403227

Reference27 articles.

1. A Novel Non-negative Matrix Factorization Method for Recommender Systems;Aghdam Mehdi Hosseinzadeh;Applied Mathematics & Information Sciences,2015

2. Eric Battenberg and David Wessel. 2009. Accelerating Non-Negative Matrix Factorization for Audio Source Separation on Multi-Core and Many-Core Architectures.. In ISMIR. 501--506. Eric Battenberg and David Wessel. 2009. Accelerating Non-Negative Matrix Factorization for Audio Source Separation on Multi-Core and Many-Core Architectures.. In ISMIR. 501--506.

3. Fast Local Algorithms for Large Scale Nonnegative Matrix and Tensor Factorizations

4. Parallel Nonnegative Matrix Factorization Algorithm on the Distributed Memory Platform

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