# Active Learning Classification from a Signal Separation Perspective

arXiv: https://arxiv.org/abs/2502.16425 · HTML: https://mlsanity.com/paper/2502.16425/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 23 Feb 2025.

## Builds on (most cited first, 4 of 4)

- [Cautious Active Clustering](https://mlsanity.com/paper/2008.01245/) (2020)
- [A witness function based construction of discriminative models using Hermite polynomials](https://mlsanity.com/paper/1901.02975/) (2019)
- [Learning on manifolds without manifold learning](https://mlsanity.com/paper/2402.12687/) (2024)
- [Robust and tractable multidimensional exponential analysis](https://mlsanity.com/paper/2404.11004/) (2024)

## Built on by (most cited first, 1 of 1)

- [A signal separation view of classification](https://mlsanity.com/paper/2509.24140/) (2025)

## Abstract (arXiv)

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.