Projects per year
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 language | English |
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Pages (from-to) | 29-37 |
Journal | Neural Networks |
Volume | 127 |
Online published | 14 Apr 2020 |
DOIs | |
Publication status | Published - Jul 2020 |
Research Keywords
- Canonical correlation analysis
- Covariance/cross-covariance operator
- Kernel method
- Random projection
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Dive into the research topics of 'Randomized sketches for kernel CCA'. Together they form a unique fingerprint.Projects
- 2 Finished
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GRF: Low-rank tensor as a Dimension Reduction Tool in Complex Data Analysis
LIAN, H. (Principal Investigator / Project Coordinator)
1/01/20 → 28/11/24
Project: Research
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GRF: Divide and Conquer in High-dimensional Statistical Models
LIAN, H. (Principal Investigator / Project Coordinator)
1/10/18 → 24/08/23
Project: Research