190 citations · 1.3k across the 74 of their papers we have counts for
90 papers
Curve Band Depth: A Band-Based Data Depth for Unparameterized Planar Curves
Siyi Wang, Alexandre Leblanc, Paul D. McNicholas
We introduce \emph{curve band depth} (CBD), a band-based data depth for samples of \emph{unparameterized} planar curves. CBD is motivated by band depth and modified band depth for…
Automatic depth-based local center clustering via -integrated local depth and adaptive grouping
Siyi Wang, Alexandre Leblanc, Paul D. McNicholas
Clustering is an unsupervised learning technique that partitions unlabeled data into groups. Most existing methods require user-specified parameters, such as the number of clusters…
Classification Fields: Arbitrarily Fine Recursive Hierarchical Clustering From Few Examples
Yicen Li, Ruiyang Hong, Anastasis Kratsios +2
Classical clustering methods usually return either a finite partition of the observed data or a finite dendrogram over it. This finite-sample view is inadequate when the hierarchy…
Turtle shell clustering: A mixture approach to discriminative clustering with applications to flow cytometry and other data
Mackenzie R. Neal, Paul D. McNicholas, Arthur White
Generative approaches to clustering provide information on geometric properties of clusters, whereas discriminative approaches provide boundaries between clusters. Ideas from both…
Neural Operators Can Discover Functional Clusters
Yicen Li, Jose Antonio Lara Benitez, Ruiyang Hong +3
Operator learning is reshaping scientific computing by amortizing inference across infinite families of problems. While neural operators (NOs) are increasingly well understood for…
funOCLUST: Clustering Functional Data with Outliers
Katharine M. Clark, Paul D. McNicholas
Functional data present unique challenges for clustering due to their infinite-dimensional nature and potential sensitivity to outliers. An extension of the OCLUST algorithm to the…