Online Low-Rank Tensor Subspace Tracking from Incomplete Data by CP Decomposition using Recursive Least Squares

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.

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2016)

References in corpus (1)

• Subspace Learning and Imputation for Streaming Big Data Matrices and Tensors

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