From the 1 of 8 linked papers with an AI index.
8 papers
funOCLUST: Clustering Functional Data with Outliers
Katharine M. Clark, Paul D. McNicholas
The paper extends the OCLUST algorithm to handle functional data, providing a robust clustering method that can also identify and trim outliers in curve datasets.
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…
Depth-Based Local Center Clustering: A Framework for Handling Different Clustering Scenarios
Siyi Wang, Alexandre Leblanc, Paul D. McNicholas
Cluster analysis, or clustering, plays a crucial role across numerous scientific and engineering domains. Despite the wealth of clustering methods proposed over the past decades, e…
Keep It Light! Simplifying Image Clustering Via Text-Free Adapters
Yicen Li, Haitz Sáez de Ocáriz Borde, Anastasis Kratsios +1
In the era of pre-trained models, effective classification can often be achieved using simple linear probing or lightweight readout layers. In contrast, many competitive clustering…