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Efficient Algorithms for Low Tubal Rank Tensor Approximation with Applications to Image Compression, Super-Resolution and Deep Learning

Autor: Ahmadi-Asl, Salman, Rezaeian, Naeim, Caiafa, Cesar F., de Almeidad, Andre L. F.

In this paper we propose efficient randomized fixed-precision techniques for low tubal rank approximation of tensors. The proposed methods are faster and more efficient than the existing fixed-precision algorithms for approximating the truncated tens

Externí odkaz: http://arxiv.org/abs/2412.02598