Learning Approach For Fast Approximate Matrix Factorizations

Efficiently computing an (approximate) orthonormal basis and low-rank approximation for the input data X plays a crucial role in data analysis. One of the most efficient algorithms for such tasks is the randomized algorithm, which proceeds by computing a projection XA with a random sketching matrix...

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Vydané v:	Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) s. 5408 - 5412
Hlavní autori:	Yu, Haiyan, Qin, Zhen, Zhu, Zhihui
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 23.05.2022
Predmet:	Acoustics Approximation algorithms Conferences learning approach Low-rank matrix approximation Signal processing Signal processing algorithms sketching algorithm Training Training data
ISSN:	2379-190X
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