Online Low-Rank Tensor Subspace Tracking from Incomplete Data by CP Decomposition using Recursive Least Squares
Numerical Analysis
2016-08-23 v2
Abstract
We propose an online tensor subspace tracking algorithm based on the CP decomposition exploiting the recursive least squares (RLS), dubbed OnLine Low-rank Subspace tracking by TEnsor CP Decomposition (OLSTEC). Numerical evaluations show that the proposed OLSTEC algorithm gives faster convergence per iteration comparing with the state-of-the-art online algorithms.
Keywords
Cite
@article{arxiv.1602.07067,
title = {Online Low-Rank Tensor Subspace Tracking from Incomplete Data by CP Decomposition using Recursive Least Squares},
author = {Hiroyuki Kasai},
journal= {arXiv preprint arXiv:1602.07067},
year = {2016}
}
Comments
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2016)