Canonical Correlation Analysis based on Hilbert-Schmidt Independence Criterion and Centered Kernel Target Alignment

51 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

NoPeek: Information leakage reduction to share activations in distributed deep learning (2020, methodology): “…case of other recent popular measures of dependence such as Hilbert-Schmidt Independence Criterion (HSIC), Maximum Mean Discrepancy (MMD) and Kernelized Mutual Information (KMI) that have been extensively studied and used recently in the machine learning and statistics community [11, 58, 59, 60].”
The exact equivalence of distance and kernel methods in hypothesis testing (2018, methodology): “The Hilbert-Schmidt independence criterion is a kernel-based method for testing independence and equally popular in related inference tasks [1, 2, 4, 6, 7, 26, 36].”
Sparse kernel canonical correlation analysis for discovery of nonlinear interactions in high-dimensional data (2017, methodology): “Another method for finding nonlinear associations is to maximize measures of nonlinear matching, such as the Hilbert-Schmidt Independent Criterion (HSIC) [14] and the Kernel Target Alignment (KTA) [15] between linearly projected datasets x and z [16].”