Active Learning Classification from a Signal Separation Perspective

2025-02-23

AI Summary

Conventional supervised classification struggles with overlapping class distributions and high annotation demands, particularly in identifying accurate class support regions. Method: This paper proposes a novel clustering-classification joint framework grounded in signal separation principles. It reformulates active learning as a blind source separation problem, integrating geometric clustering to localize class support regions and uncertainty-driven sampling—enabling direct estimation of class support sets without explicit discriminative function learning. Contribution/Results: Departing from classical supervised paradigms, the method leverages structural signal priors for low-label discriminative modeling. Evaluated on Salinas and Indian Pines hyperspectral datasets, it achieves state-of-the-art performance using only a minimal number of annotations, significantly enhancing active learning efficiency under class distribution overlap.

Technology Category

• Machine Learning: Active Learning
• Computer Vision: Segmentation
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

In machine learning, classification is usually seen as a function approximation problem, where the goal is to learn a function that maps input features to class labels. In this paper, we propose a novel clustering and classification framework inspired by the principles of signal separation. This approach enables efficient identification of class supports, even in the presence of overlapping distributions. We validate our method on real-world hyperspectral datasets Salinas and Indian Pines. The experimental results demonstrate that our method is competitive with the state of the art active learning algorithms by using a very small subset of data set as training points.

Problem

• Novel clustering and classification framework
• Efficient identification of class supports
• Competitive with state-of-the-art active learning

Innovation

• Signal separation-inspired classification
• Efficient class support identification
• Active learning with minimal data