August 11, 2023
Dictionary learning, which approximates data samples by a set of shared atoms, is a fundamental task in representation learning. However, dictionary learning over graphs, namely graph dictionary learning (GDL), is much more challenging than vectorial data as graphs lie in disparate metric spaces. The sparse literature on GDL formulates the problem from the reconstructive view and often learns linear graph embeddings with a high computational cost. In this paper, we propose a Fused Gromov-Wasserstein (FGW) Mixture Model named FraMe to address the GDL problem from the generative view. Equipped with the graph generation function based on the radial basis function kernel and FGW distance, FraMe generates nonlinear embedding spaces, which, as we theoretically proved, provide a good approximation of the original graph spaces. A fast solution is further proposed on top of the expectation-maximization algorithm with guaranteed convergence. Extensive experiments demonstrate the effectiveness of the obtained node and graph embeddings, and our algorithm achieves significant improvements over the state-of-the-art methods.
Publisher
ICML
Research Topics
Core Machine Learning
February 15, 2024
Danny Deng, Hongkuan Zhou, Hanqing Zeng, Yinglong Xia, Chris Leung (AI), Jianbo Li, Rajgopal Kannan, Viktor Prasanna
February 15, 2024
February 15, 2024
Adrien Bardes, Quentin Garrido, Xinlei Chen, Michael Rabbat, Yann LeCun, Mido Assran, Nicolas Ballas, Jean Ponce
February 15, 2024
January 09, 2024
Less Wright, Adnan Hoque
January 09, 2024
January 06, 2024
Geng Ji, Wentao Jiang, Jiang Li, Fahmid Morshed Fahid, Zhengxing Chen, Yinghua Li, Jun Xiao, Chongxi Bao, Zheqing (Bill) Zhu
January 06, 2024
Product experiences
Foundational models
Product experiences
Latest news
Foundational models