Robust Classification of Graph-Based Data

TL;DR

This paper introduces a robust semi-supervised classification method for graph-based data using a convex optimization approach with a concave loss function, enhancing noise robustness and aligning more closely with classification objectives.

Contribution

It presents a novel convex optimization framework with a concave loss for improved robustness in graph-based semi-supervised learning.

Findings

Enhanced robustness to noisy labels

Convex formulation ensures stable optimization

Better alignment with classification tasks

Abstract

A graph-based classification method is proposed for semi-supervised learning in the case of Euclidean data and for classification in the case of graph data. Our manifold learning technique is based on a convex optimization problem involving a convex quadratic regularization term and a concave quadratic loss function with a trade-off parameter carefully chosen so that the objective function remains convex. As shown empirically, the advantage of considering a concave loss function is that the learning problem becomes more robust in the presence of noisy labels. Furthermore, the loss function considered here is then more similar to a classification loss while several other methods treat graph-based classification problems as regression problems.