用概率方法优化图切割，提升聚类效果
Deep Clustering via Probabilistic Ratio-Cut Optimization
- 将二值分配建模为随机变量，通过梯度学习参数
- 在多个数据集上优于传统松弛法和在线学习方法
- 可结合自监督表示，适合评估表征质量
我们提出一种新方法，通过将二值分配建模为随机变量来优化图的比率切割（ratio-cut）。提供了期望比率切割的上界以及其梯度的无偏估计，可在在线设置下学习分配变量的参数。基于概率框架的聚类方法（PRCut）在多个数据集上表现优于比经典组合问题的瑞利商松弛法、其在线学习扩展以及多种常用聚类方法。实验表明，PRCut聚类结果与相似性度量高度一致，当提供标签相似性时，性能可媲美监督分类器。该方法能直接使用现成的自监督表示，实现竞争力表现，并可用于评估这些表示的质量。
原文摘要 · Abstract (English)
We propose a novel approach for optimizing the graph ratio-cut by modeling the binary assignments as random variables. We provide an upper bound on the expected ratio-cut, as well as an unbiased estimate of its gradient, to learn the parameters of the assignment variables in an online setting. The clustering resulting from our probabilistic approach (PRCut) outperforms the Rayleigh quotient relaxation of the combinatorial problem, its online learning extensions, and several widely used methods. We demonstrate that the PRCut clustering closely aligns with the similarity measure and can perform as well as a supervised classifier when label-based similarities are provided. This novel approach can leverage out-of-the-box self-supervised representations to achieve competitive performance and serve as an evaluation method for the quality of these representations.
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