Sublinear Models for Graphs
Machine Learning
2014-03-11 v1 Computer Vision and Pattern Recognition
Abstract
This contribution extends linear models for feature vectors to sublinear models for graphs and analyzes their properties. The results are (i) a geometric interpretation of sublinear classifiers, (ii) a generic learning rule based on the principle of empirical risk minimization, (iii) a convergence theorem for the margin perceptron in the sublinearly separable case, and (iv) the VC-dimension of sublinear functions. Empirical results on graph data show that sublinear models on graphs have similar properties as linear models for feature vectors.
Cite
@article{arxiv.1403.2295,
title = {Sublinear Models for Graphs},
author = {Brijnesh J. Jain},
journal= {arXiv preprint arXiv:1403.2295},
year = {2014}
}