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 | Signal Processing |
| 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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