Sublinear Models for Graphs [article]

Brijnesh J. Jain
2014 arXiv   pre-print
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.
arXiv:1403.2295v1 fatcat:j47l2qnm2bhdbcov3ihag3hi2a