Randomized sketches for kernel CCA

Heng Lian*, Fode Zhang, Wenqi Lu

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

5 Citations (Scopus)

Abstract

Kernel canonical correlation analysis (KCCA) is a popular tool as a nonlinear extension of canonical correlation analysis. Consistency and optimal convergence rate have been established in the literature. However, the time complexity of KCCA scales as O(n3) and is thus prohibitive when n is large. We propose an m-dimensional randomized sketches approach for KCCA with m << n, based on the recent work on randomized sketches for kernel ridge regression (KRR). Technically we establish our theoretical results relying on an interesting connection between KCCA and KRR by utilizing a novel “duality tracking” device that alternates between the infinite-dimensional operator-theory-based view of KCCA and the finite-dimensional kernel-matrix-based view.
Original languageEnglish
Pages (from-to)29-37
JournalNeural Networks
Volume127
Online published14 Apr 2020
DOIs
Publication statusPublished - Jul 2020

Research Keywords

  • Canonical correlation analysis
  • Covariance/cross-covariance operator
  • Kernel method
  • Random projection

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