ALEVS: Active Learning by Statistical Leverage Sampling

TL;DR

This paper introduces a novel active learning method called ALEVS that uses statistical leverage scores to select influential data points, improving classifier accuracy with fewer labeled samples.

Contribution

The paper proposes leveraging statistical leverage scores as a new criterion for query selection in active learning, demonstrating its effectiveness over traditional methods.

Findings

High leverage points lead to improved classification accuracy.

Sampling based on leverage scores outperforms random sampling.

The method is effective across multiple binary datasets.

Abstract

Active learning aims to obtain a classifier of high accuracy by using fewer label requests in comparison to passive learning by selecting effective queries. Many active learning methods have been developed in the past two decades, which sample queries based on informativeness or representativeness of unlabeled data points. In this work, we explore a novel querying criterion based on statistical leverage scores. The statistical leverage scores of a row in a matrix are the squared row-norms of the matrix containing its (top) left singular vectors and is a measure of influence of the row on the matrix. Leverage scores have been used for detecting high influential points in regression diagnostics and have been recently shown to be useful for data analysis and randomized low-rank matrix approximation algorithms. We explore how sampling data instances with high statistical leverage scores perform in active learning. Our empirical comparison on several binary classification datasets indicate that querying high leverage points is an effective strategy.