Unsupervised Pool-Based Active Learning for Linear Regression

TL;DR

This paper introduces an unsupervised pool-based active learning method for linear regression that selects initial samples without label information, focusing on informativeness, representativeness, and diversity to improve model training.

Contribution

It presents a novel unsupervised active learning approach for linear regression that does not require labeled data for initial sample selection, addressing a key challenge in AL.

Findings

Effective across 14 diverse datasets

Works with ridge regression, LASSO, and linear SVR

Outperforms baseline methods in sample selection

Abstract

In many real-world machine learning applications, unlabeled data can be easily obtained, but it is very time-consuming and/or expensive to label them. So, it is desirable to be able to select the optimal samples to label, so that a good machine learning model can be trained from a minimum amount of labeled data. Active learning (AL) has been widely used for this purpose. However, most existing AL approaches are supervised: they train an initial model from a small amount of labeled samples, query new samples based on the model, and then update the model iteratively. Few of them have considered the completely unsupervised AL problem, i.e., starting from zero, how to optimally select the very first few samples to label, without knowing any label information at all. This problem is very challenging, as no label information can be utilized. This paper studies unsupervised pool-based AL for linear regression problems. We propose a novel AL approach that considers simultaneously the informativeness, representativeness, and diversity, three essential criteria in AL. Extensive experiments on 14 datasets from various application domains, using three different linear regression models (ridge regression, LASSO, and linear support vector regression), demonstrated the effectiveness of our proposed approach.