Constructing low-rank Tucker tensor approximations using generalized completion

Pub Date : 2024-04-08 DOI:10.1515/rnam-2024-0010
Sergey Petrov
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Abstract

The projected gradient method for matrix completion is generalized towards the higher-dimensional case of low-rank Tucker tensors. It is shown that an operation order rearrangement in the common projected gradient approach provides a complexity improvement. An even better algorithm complexity can be obtained by replacing the completion operator by a general operator that satisfies restricted isometry property; however, such a replacement transforms the completion algorithm into an approximation algorithm.
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利用广义补全构建低阶塔克张量近似值
针对低阶塔克张量的高维情况,对矩阵补全的投影梯度法进行了推广。结果表明,普通投影梯度法中的运算顺序重排可以提高复杂度。用一个满足受限等距性质的一般算子代替补全算子,可以获得更好的算法复杂度;但是,这种替换会将补全算法转化为近似算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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