Lower memory oblivious (tensor) subspace embeddings with fewer random bits: Modewise methods for least squares

Mark A. Iwen, Deanna Needell, Elizaveta Rebrova, And A.L.I. Zare

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper new general modewise Johnson-Lindenstrauss (JL) subspace embeddings are proposed that can be both generated much faster and stored more easily than traditional JL embeddings when working with extremely large vectors and/or tensors. Corresponding embedding results are then proven for two different types of low-dimensional (tensor) subspaces. The first of these new subspace embedding results produces improved space complexity bounds for embeddings of rank-r tensors whose CP decompositions are contained in the span of a fixed (but unknown) set of r rank-1 basis tensors. In the traditional vector setting this first result yields new and very general near-optimal oblivious subspace embedding constructions that require fewer random bits to generate than standard JL embeddings when embedding subspaces of CN spanned by basis vectors with special Kronecker structure. The second result proven herein provides new fast JL embeddings of arbitrary r-dimensional subspaces S ⊆ CN which also require fewer random bits (and so are easier to store, i.e., require less space) than standard fast JL embedding methods in order to achieve small ∊ - distortions. These new oblivious subspace embedding results work by (i) effectively folding any given vector in S into a (not necessarily low-rank) tensor, and then (ii) embedding the resulting tensor into Cm for m ≤ Cr logc(N)/∊2. Applications related to compression and fast compressed least squares solution methods are also considered, including those used for fitting low-rank CP decompositions, and the proposed JL embedding results are shown to work well numerically in both settings.

Original languageEnglish (US)
Pages (from-to)376-416
Number of pages41
JournalSIAM Journal on Matrix Analysis and Applications
Volume42
Issue number1
DOIs
StatePublished - 2021
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Analysis

Keywords

  • CP decompositions
  • Dimensionality reduction
  • Fast approximation algorithms
  • Johnson-Lindenstrauss embeddings
  • Least squares fitting
  • Low-rank tensors
  • Tensors

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