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- W4313492041 abstract "We consider the problem of learning tensors from partial observations with structural constraints, under the low-rank assumption. To this end, we propose a general convex low-rank regularizer, parameterized by linear maps, that extends the existing regularizers. Different choices of the linear maps lead to different convex low-rank regularizers. The resulting problem is called the generalized structured low-rank tensor learning problem. To solve this problem, we use duality theory to reformulate it into simpler sub-problems. The dual problem contains a rich geometric structure, which we exploit to develop first-order and second-order Riemannian optimization algorithms. The associated duality gap is derived, and it is shown to be zero. Moreover, we experimentally verify the correctness of our algorithm on several special cases of the proposed general framework." @default.
- W4313492041 created "2023-01-06" @default.
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- W4313492041 date "2023-01-04" @default.
- W4313492041 modified "2023-09-23" @default.
- W4313492041 title "Generalized Structured Low-Rank Tensor Learning" @default.
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- W4313492041 doi "https://doi.org/10.1145/3570991.3571022" @default.
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