Online low-rank tensor subspace tracking from incomplete data by CP decomposition using recursive least squares

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Authors H. Kasai
Journal/Conference Name 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Paper Category
Paper 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.
Date of publication 2016
Code Programming Language Matlab
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