A Riemannian Rank‐Adaptive Method for Higher‐Order Tensor Completion in the Tensor‐Train Format
Charlotte Vermeylen,
Marc Van Barel
Abstract:A new Riemannian rank adaptive method (RRAM) is proposed for the low‐rank tensor completion problem (LRTCP). This problem is formulated as a least‐squares optimization problem on the algebraic variety of tensors of bounded tensor‐train (TT) rank. The RRAM iteratively optimizes over fixed‐rank smooth manifolds using a Riemannian conjugate gradient algorithm from Steinlechner. In between, the rank is increased by computing a descent direction selected in the tangent cone to the variety. A numerical method to est… Show more
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