RankMap: A Framework for Distributed Learning From Dense Data Sets

This paper introduces RankMap, a platform-aware end-to-end framework for efficient execution of a broad class of iterative learning algorithms for massive and dense data sets. Our framework exploits data structure to scalably factorize it into an ensemble of lower rank subspaces. The factorization c...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 29; H. 7; S. 2717 - 2730
Hauptverfasser:	Mirhoseini, Azalia, Dyer, Eva L., Songhori, Ebrahim M., Baraniuk, Richard, Koushanfar, Farinaz
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.07.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Computational modeling Computer networks Data recovery Data structures Datasets Dense and big data Distributed databases Distributed processing Iterative algorithms iterative machine learning (ML) Iterative methods large-scale distributed computing Learning algorithms low rank approximation Machine learning Matrix decomposition Memory Partitioning algorithms Signal processing algorithms Sparse matrices sparse matrix factorization Subspaces union of subspaces
ISSN:	2162-237X, 2162-2388, 2162-2388
Online-Zugang:	Volltext
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