An Improved Frequent Directions Algorithm for Low-Rank Approximation via Block Krylov Iteration

Frequent directions (FDs), as a deterministic matrix sketching technique, have been proposed for tackling low-rank approximation problems. This method has a high degree of accuracy and practicality but experiences a lot of computational cost for large-scale data. Several recent works on the randomiz...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 35; H. 7; S. 9428 - 9442
Hauptverfasser:	Wang, Chenhao, Yi, Qianxin, Liao, Xiuwu, Wang, Yao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.07.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accuracy Algorithms Approximation Approximation algorithms Approximation error Block Krylov iteration Computational efficiency Computer applications Computing costs Cost analysis Costs Efficiency Error analysis frequent directions (FDs) low-rank approximation Neural networks Principal component analysis randomized sketching Signal processing algorithms Sparse matrices streaming algorithms Theoretical analysis
ISSN:	2162-237X, 2162-2388, 2162-2388
Online-Zugang:	Volltext
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