Randomized sketches for kernel CCA.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 32311655.
- Also identified by DOI 10.1016/j.neunet.2020.04.006.
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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(n<sup>3</sup>) 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.
Medical subject headings
- Spatial Analysis