Learning Generative Embeddings using an Optimal Subsampling Policy for Tensor Sketching. (arXiv:2209.00372v1 [cs.LG])

Data tensors of orders 3 and greater are routinely being generated. These
data collections are increasingly huge and growing. They are either tensor
fields (e.g., images, videos, geographic data) in which each location of data
contains important information or permutation invariant general tensors (e.g.,
unsupervised latent space learning, graph network analysis, recommendation
systems, etc.). Directly accessing such large data tensor collections for
information has become increasingly prohibitive. We learn approximate full-rank
and compact tensor sketches with decompositive representations providing
compact …

arxiv learning policy tensor