从信号分离角度重构分类，小样本下仍高效识别类别分布。
Active Learning Classification from a Signal Separation Perspective
- 将分类视为信号分离问题，通过聚类挖掘类别支撑结构。
- 仅用极小数据子集训练，即达顶尖主动学习性能。
- 适合高维遥感数据，尤其在类别重叠时表现优异。
在机器学习中，分类通常被视为函数逼近问题，目标是学习将输入特征映射到类别标签的函数。本文提出一种受信号分离原理启发的新型聚类与分类框架，可在类别分布重叠情况下高效识别各类别支撑区域。我们在真实世界高光谱数据集Salinas和Indian Pines上验证了该方法，实验表明，仅需极小规模的数据子集作为训练点，其性能即可媲美当前最先进的主动学习算法。
原文摘要 · Abstract (English)
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.
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