Scalable Clustering: Large Scale Unsupervised Learning of Gaussian Mixture Models with Outliers
提出一种基于损失最小化的鲁棒聚类算法，理论保证在高斯混合模型含离群点时高概率获得高精度，可作为k-means初始化策略，在ImageNet等大规模数据集上速度和精度优于经典方法。
Clustering is a widely used technique with a long and rich history in a variety of areas. However, most existing algorithms do not scale well to large datasets, or are missing theoretical guarantees of convergence. This paper introduces a provably robust clustering algorithm based on loss minimization that performs well on Gaussian mixture models with outliers. It provides theoretical guarantees that the algorithm obtains high accuracy with high probability under certain assumptions. Moreover, it can also be used as an initialization strategy for k-means clustering. Experiments on real-world large-scale datasets demonstrate the effectiveness of the algorithm when clustering a large number of clusters, and a k-means algorithm initialized by the algorithm outperforms many of the classic clustering methods in both speed and accuracy, while scaling well to large datasets such as ImageNet.