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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Veröffentlicht in:	Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) S. 5408 - 5412
Hauptverfasser:	Yu, Haiyan, Qin, Zhen, Zhu, Zhihui
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 23.05.2022
Schlagworte:	Acoustics Approximation algorithms Conferences learning approach Low-rank matrix approximation Signal processing Signal processing algorithms sketching algorithm Training Training data
ISSN:	2379-190X
Online-Zugang:	Volltext
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