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Tensor Regression Meets Gaussian Processes by Yu, Rose; Li, Guangyu; Liu, Yan is a scholarly article available to read on EtoBox.

What is Tensor Regression Meets Gaussian Processes about?

Low-rank tensor regression, a new model class that learns high-order correlation from data, has recently received considerable attention. At the same time, Gaussian processes (GP) are well-studied machine learning models for structure learning. In this paper, we demonstrate interesting connections between the two, especially for multi-way data analysis. We show that low-rank tensor regression is essentially learning a multi-linear kernel in Gaussian processes, and the low-rank assumption translates to the constrained Bayesian inference problem. We prove the oracle inequality and derive the average case learning curve for the equivalent GP model. Our finding implies that low-rank tensor regression, though empirically successful, is highly dependent on the eigenvalues of covariance functions as well as variable correlations.

Author

Yu, Rose; Li, Guangyu; Liu, Yan

Published

2017

Language

EN