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• W3208596757 abstract "Random features approach has been widely used for kernel approximation in large-scale machine learning. A number of recent studies have explored data-dependent sampling of features, modifying the stochastic oracle from which random features are sampled. While proposed techniques in this realm improve the approximation, their suitability is often verified on a single learning task. In this paper, we propose a task-specific scoring rule for selecting random features, which can be employed for different applications with some adjustments. We restrict our attention to Canonical Correlation Analysis (CCA), and we provide a novel, principled guide for finding the score function maximizing the canonical correlations. We prove that this method, called ORCCA, can outperform (in expectation) the corresponding Kernel CCA with a default kernel. Numerical experiments verify that ORCCA is significantly superior than other approximation techniques in the CCA task." @default.
• W3208596757 created "2021-11-08" @default.
• W3208596757 creator A5040922600 @default.
• W3208596757 creator A5069098060 @default.
• W3208596757 date "2019-10-11" @default.
• W3208596757 modified "2023-09-24" @default.
• W3208596757 title "ORCCA: Optimal Randomized Canonical Correlation Analysis" @default.
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• W3208596757 doi "https://doi.org/10.48550/arxiv.1910.05384" @default.
• W3208596757 hasPublicationYear "2019" @default.
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