# A Practical and Provable Algorithm for Subspace Clustering

1. Introduction Traditional distance based clustering algorithms such as K-Means perform poorly on high dimensional data because of curse of dimensionality[1]. A common approach to cluster such data is to assume that even though their ambient dimensionality is high, their intrinsic dimensionality is much lower. As an example, consider the dataset of images of faces… Continue reading A Practical and Provable Algorithm for Subspace Clustering