将时频图信号分离转化为图聚类问题，提出新方法。
Time-Frequency Filtering Meets Graph Clustering
- 用图聚类思想重构时频表示中的信号成分识别
- 实验验证了该方法在多信号成分分离中的有效性
- 适合从事信号处理与图学习交叉研究者阅读
我们表明，从时频表示中识别不同信号成分的问题可等价地表述为图聚类问题：给定图 $G=(V,E)$，目标是识别出内部连接紧密、彼此间连接稀疏的子图（即‘簇’）。图聚类问题已有深入研究，本文展示了如何利用这些思想提出（多种）新的信号成分识别方法。数值实验验证了所提思路的有效性。
原文摘要 · Abstract (English)
We show that the problem of identifying different signal components from a time-frequency representation can be equivalently phrased as a graph clustering problem: given a graph $G=(V,E)$ one aims to identify `clusters', subgraphs that are strongly connected and have relatively few connections between them. The graph clustering problem is well studied, we show how these ideas can suggest (many) new ways to identify signal components. Numerical experiments illustrate the ideas.
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