随机均值漂移聚类提升效率，实测优于传统方法。
Stochastic Mean-Shift Clustering
- 随机选取数据点按梯度上升步长移动，实现高效聚类。
- 在二维高斯混合数据上，多数情况性能优于标准均值漂移。
- 适用于语音说话人聚类等实际场景。
我们提出一种随机均值漂移聚类算法，该方法通过随机选择数据点序列，沿目标函数的局部梯度上升方向进行迭代更新。理论分析表明该方法具有收敛性。在由高斯混合分布生成的二维合成数据集上，与当前主流方法对比，该方法在多数情况下表现更优。此外，我们还展示了该方法在说话人聚类中的实际应用效果。
原文摘要 · Abstract (English)
We present a stochastic version of the mean-shift clustering algorithm. In this stochastic version a randomly chosen sequence of data points move according to partial gradient ascent steps of the objective function. Theoretical results illustrating the convergence of the proposed approach, and its relative performances is evaluated on synthesized 2-dimensional samples generated by a Gaussian mixture distribution and compared with state-of-the-art methods. It can be observed that in most cases the stochastic mean-shift clustering outperforms the standard mean-shift. We also illustrate as a practical application the use of the presented method for speaker clustering.
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