Unsupervised Projected Sample Selector for Active Learning

Abstract

Active learning, as a technique, aims to effectively label specific data points while operating within a designated query budget. Nevertheless, the majority of unsupervised active learning algorithms are based on shallow linear representation and lack sufficient interpretability. Furthermore, certain diversity-based methods face challenges in selecting samples that adequately represent the entire data distribution. Inspired by these reasons, in this paper, we propose an unsupervised active learning method on orthogonal projections to construct a deep neural network model. By optimizing the orthogonal projection process, we establish the connection between projection and active learning, consequently enhancing the interpretability of the proposed method. The proposed method can efficiently project the feature space onto a spanned subspace, deriving an indicator matrix while calculating the projection loss. Moreover, we consider the redundancy among samples to ensure both data point diversity and enhancement of clustering-based algorithms. Through extensive comparative experiments on six public datasets, the results demonstrate that the proposed method can effectively select more informative and representative samples and improve performance by up to 11%.