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Randomized sketches for kernel CCA
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AI
DOI：10.1016/j.neunet.2020.04.006.png)
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Abstract
En 中文
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(3)) 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. (C) 2020 Elsevier Ltd. All rights reserved.
Keywords:
Canonical correlation analysis
Covariance/cross-covariance operator
Kernel method
Random projection
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