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Found 5 papers, 0 papers with code
Re-embedding data to strengthen recovery guarantees of clustering
We propose a clustering method that involves chaining four known techniques into a pipeline yielding an algorithm with stronger recovery guarantees than any of the four components separately.
Robust Correlation Clustering with Asymmetric Noise
Graph clustering problems typically aim to partition the graph nodes such that two nodes belong to the same partition set if and only if they are similar.
Certifying clusters from sum-of-norms clustering
Multiple algorithms have been proposed to solve the optimization problem: subgradient descent by Hocking et al., ADMM and ADA by Chi and Lange, stochastic incremental algorithm by Panahi et al. and semismooth Newton-CG augmented Lagrangian method by Sun et al. All algorithms yield approximate solutions, even though an exact solution is demanded to determine the correct cluster assignment.
Provable Overlapping Community Detection in Weighted Graphs
When the communities are allowed to overlap, often a pure nodes assumption is made, i. e. each community has a node that belongs exclusively to that community.
Recovery of a mixture of Gaussians by sum-of-norms clustering
Sum-of-norms clustering is a method for assigning $n$ points in $\mathbb{R}^d$ to $K$ clusters, $1\le K\le n$, using convex optimization.
