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arXiv · 1710.06693

First-Order Perturbation Analysis of the SECSI Framework for the Approximate CP Decomposition of 3-D Noise-Corrupted Low-Rank Tensors

Sher Ali Cheema·Emilio Rafael Balda·Yao Cheng·Martin Haardt·Amir Weiss·Arie Yeredor
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Abstract

The Semi-Algebraic framework for the approximate Canonical Polyadic (CP) decomposition via SImultaneaous matrix diagonalization (SECSI) is an efficient tool for the computation of the CP decomposition. The SECSI framework reformulates the CP decomposition into a set of joint eigenvalue decomposition (JEVD) problems. Solving all JEVDs, we obtain multiple estimates of the factor matrices and the best estimate is chosen in a subsequent step by using an exhaustive search or some heuristic strategy that reduces the computational complexity. Moreover, the SECSI framework retains the option of choosing the number of JEVDs to be solved, thus providing an adjustable complexity-accuracy trade-off. In this work, we provide an analytical performance analysis of the SECSI framework for the computation of the approximate CP decomposition of a noise corrupted low-rank tensor, where we derive closed-form expressions of the relative mean square error for each of the estimated factor matrices. These expressions are obtained using a first-order perturbation analysis and are formulated in terms of the second-order moments of the noise, such that apart from a zero mean, no assumptions on the noise statistics are required. Simulation results exhibit an excellent match between the obtained closed-form expressions and the empirical results. Moreover, we propose a new Performance Analysis based Selection (PAS) scheme to choose the final factor matrix estimate. The results show that the proposed PAS scheme outperforms the existing heuristics, especially in the high SNR regime.

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Sher Ali Cheema, Emilio Rafael Balda, Yao Cheng, Martin Haardt, Amir Weiss, Arie Yeredor. 2017-10-18. First-Order Perturbation Analysis of the SECSI Framework for the Approximate CP Decomposition of 3-D Noise-Corrupted Low-Rank Tensors. https://arxiv.org/abs/1710.06693

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