Scaling and Scalability: Provable Nonconvex Low-Rank Tensor Estimation from Incomplete Measurements. (arXiv:2104.14526v3 [cs.LG] UPDATED)

Web: http://arxiv.org/abs/2104.14526

Tensors, which provide a powerful and flexible model for representing
multi-attribute data and multi-way interactions, play an indispensable role in
modern data science across various fields in science and engineering. A
fundamental task is to faithfully recover the tensor from highly incomplete
measurements in a statistically and computationally efficient manner.
Harnessing the low-rank structure of tensors in the Tucker decomposition, this
paper develops a scaled gradient descent (ScaledGD) algorithm to directly
recover the tensor factors with tailored spectral initializations, and …

arxiv lg scalability scaling tensor