all AI news
Tensor Principal Component Analysis in High Dimensional CP Models. (arXiv:2108.04428v3 [stat.ML] UPDATED)
Web: http://arxiv.org/abs/2108.04428
May 9, 2022, 1:11 a.m. | Yuefeng Han, Cun-Hui Zhang
cs.LG updates on arXiv.org arxiv.org
The CP decomposition for high dimensional non-orthogonal spiked tensors is an
important problem with broad applications across many disciplines. However,
previous works with theoretical guarantee typically assume restrictive
incoherence conditions on the basis vectors for the CP components. In this
paper, we propose new computationally efficient composite PCA and concurrent
orthogonalization algorithms for tensor CP decomposition with theoretical
guarantees under mild incoherence conditions. The composite PCA applies the
principal component or singular value decompositions twice, first to a matrix
unfolding …
More from arxiv.org / cs.LG updates on arXiv.org
Latest AI/ML/Big Data Jobs
Data Analyst, Patagonia Action Works
@ Patagonia | Remote
Data & Insights Strategy & Innovation General Manager
@ Chevron Services Company, a division of Chevron U.S.A Inc. | Houston, TX
Faculty members in Research areas such as Bayesian and Spatial Statistics; Data Privacy and Security; AI/ML; NLP; Image and Video Data Analysis
@ Ahmedabad University | Ahmedabad, India
Director, Applied Mathematics & Computational Research Division
@ Lawrence Berkeley National Lab | Berkeley, Ca
Business Data Analyst
@ MainStreet Family Care | Birmingham, AL
Assistant/Associate Professor of the Practice in Business Analytics
@ Georgetown University McDonough School of Business | Washington DC