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Learning Graph Laplacian from Intrinsic Patterns via Gaussian Process by Koshi Watanabe; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama is a scholarly article available to read on EtoBox.

What is Learning Graph Laplacian from Intrinsic Patterns via Gaussian Process about?

In this paper, we present a novel scheme to learn a graph topology named Laplacian constrained Gaussian process (LCGP). Previous graph learning methods directly use raw observed signals, which often degrades estimation accuracy due to the noise effects and the sample imbalances. LCGP tackles this problem by introducing a small number of intrinsic patterns, that is, LCGP assumes representative signals on a graph. These signals are derived as Bayesian latent variables, and we estimate the graph topology with objective after marginalizing them. Furthermore, the number of the intrinsic patterns is automatically determined by a Bayesian inference procedure. In the experiment, we compared LCGP with baseline and state-of-the-art methods in graph learning and conrmed the effectiveness of LCGP.

Author

Koshi Watanabe; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama

Publisher

IEEE

Published

2023

Language

EN