Low Rank Approximation and Decomposition of Large Matrices Using Error Correcting Codes

Low rank approximation is an important tool used in many applications of signal processing and machine learning. Recently, randomized sketching algorithms were proposed to effectively construct low rank approximations and obtain approximate singular value decompositions of large matrices. Similar id...

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Veröffentlicht in:	IEEE transactions on information theory Jg. 63; H. 9; S. 5544 - 5558
Hauptverfasser:	Ubaru, Shashanka, Mazumdar, Arya, Saad, Yousef
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.09.2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithm design and analysis Approximation Approximation algorithms Columns (structural) Decomposition Error correcting codes Error correction Error correction & detection Error correction codes Least squares method low rank approximation Machine learning Mathematical analysis Matrix decomposition Matrix methods randomized sketching algorithms Regression Regression analysis Signal processing Sparse matrices Statistical analysis subspace embedding Transforms
ISSN:	0018-9448, 1557-9654
Online-Zugang:	Volltext
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