Thu, 23 July, 2009, 17:15-18:15, Foyer
Tensor decomposition has been applied in many fields, such as, signal processing, data mining, chemometrics, and scientific computing. To facilitate the applicability of tensor analysis, the numerical techniques must improve to accommodate new data. We present a new numerical method for tensor decomposition. The method is based on the iterated Tikhonov regularization and a parameter choice rule. Together these elements dramatically accelerate the well-known Alternating Least-Squares method. In most techniques, choosing the regularization parameter requires the noise level. In this paper, we use the quasi-optimality criterion to find the regularization parameter without requiring knowledge of the noise level.
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