Abstract: High-dimensional data is usually drawn from the union of multiple low-dimensional subspaces. The task of subspace clustering is to cluster high-dimensional data into their corresponding ...

Correlation clustering is a framework for partitioning the nodes of a graph according to pairwise similarity and dissimilarity labels on edges. Rather than fixing the number of clusters in advance, ...

Abstract: We introduce a novel framework for clustering a collection of tall matrices based on their column spaces, a problem we term Subspace Clustering of Subspaces (SCoS). Unlike traditional ...

Local coupled cluster approaches have emerged as a cornerstone in quantum chemistry, enabling high-precision modelling of electron correlation in large molecular systems with dramatically reduced ...