Active Robust Learning

TL;DR

This paper introduces a novel active learning method that accounts for noisy instances by proposing a new loss function and classifier, improving robustness and reducing bias in data selection.

Contribution

It proposes a new loss function and classifier that explicitly handle noisy instances, integrating instance complexity into active learning.

Findings

The new loss function reduces the impact of noisy instances.

The Simple-Complex Classifier effectively identifies noisy versus ordinary instances.

Convex relaxation enables practical optimization of the complex learning problem.

Abstract

In many practical applications of learning algorithms, unlabeled data is cheap and abundant whereas labeled data is expensive. Active learning algorithms developed to achieve better performance with lower cost. Usually Representativeness and Informativeness are used in active learning algoirthms. Advanced recent active learning methods consider both of these criteria. Despite its vast literature, very few active learning methods consider noisy instances, i.e. label noisy and outlier instances. Also, these methods didn't consider accuracy in computing representativeness and informativeness. Based on the idea that inaccuracy in these measures and not taking noisy instances into consideration are two sides of a coin and are inherently related, a new loss function is proposed. This new loss function helps to decrease the effect of noisy instances while at the same time, reduces bias. We defined "instance complexity" as a new notion of complexity for instances of a learning problem. It is proved that noisy instances in the data if any, are the ones with maximum instance complexity. Based on this loss function which has two functions for classifying ordinary and noisy instances, a new classifier, named "Simple-Complex Classifier" is proposed. In this classifier there are a simple and a complex function, with the complex function responsible for selecting noisy instances. The resulting optimization problem for both learning and active learning is highly non-convex and very challenging. In order to solve it, a convex relaxation is proposed.