Unsupervised Pool-Based Active Learning for Linear Regression
This addresses the challenge of selecting optimal initial samples to label in data-scarce scenarios, which is incremental as it extends active learning to a fully unsupervised setting for linear regression.
The paper tackles the unsupervised pool-based active learning problem for linear regression, where no initial labeled data is available, by proposing a novel approach that considers informativeness, representativeness, and diversity, and demonstrates its effectiveness through extensive experiments on 14 datasets with three linear regression models.
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.