Paper Info
Title
Semi-Supervised Learning Based On Joint Diffusion Of Graph Functions And Laplacians
Abstract
We observe the distances between estimated function outputs on data points to create an anisotropic graph Laplacian which, through an iterative process, can itself be regularized. Our algorithm is instantiated as a discrete regularizer on a graph's diffusivity operator. This idea is grounded in the theory that regularizing the diffusivity operator corresponds to regularizing the metric on Riemannian manifolds, which further corresponds to regularizing the anisotropic Laplace-Beltrami operator. We show that our discrete regularization framework is consistent in the sense that it converges to (continuous) regularization on underlying data generating manifolds. In semi-supervised learning experiments, across ten standard datasets, our diffusion of Laplacian approach has the lowest average error rate of eight different established and state-of-the-art approaches, which shows the promise of our approach.
Year
DOI
Venue
2016
10.1007/978-3-319-46454-1_43
COMPUTER VISION - ECCV 2016, PT V
Keywords
Field
DocType
Semi-supervised learning, Graph Laplacia, Diffusion, Regularization
Applied mathematics,Semi-supervised learning,Mathematical analysis,Regularization (mathematics),Artificial intelligence,Operator (computer programming),Manifold,Data point,Computer vision,Laplacian matrix,Graph of a function,Mathematics,Laplace operator
Conference
Volume
ISSN
Citations 
9909
0302-9743
0
PageRank 
References 
Authors
0.34
17
1
Name
Order
Citations
PageRank
Kwang In Kim1162578.90