Scalable Clustering: Large Scale Unsupervised Learning of Gaussian Mixture Models with Outliers
This addresses the scalability and robustness issues in clustering for large-scale data applications, representing an incremental improvement with specific gains.
The paper tackles the problem of scaling clustering algorithms to large datasets with theoretical guarantees, introducing a robust algorithm for Gaussian mixture models with outliers that achieves high accuracy under certain assumptions and can initialize k-means, outperforming classic methods in speed and accuracy on datasets like ImageNet.
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.