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Found 6 papers, 0 papers with code
Embedding Functional Data: Multidimensional Scaling and Manifold Learning
We adapt concepts, methodology, and theory originally developed in the areas of multidimensional scaling and dimensionality reduction for multivariate data to the functional setting.
Clustering by Hill-Climbing: Consistency Results
We consider several hill-climbing approaches to clustering as formulated by Fukunaga and Hostetler in the 1970's.
An Asymptotic Equivalence between the Mean-Shift Algorithm and the Cluster Tree
Two important nonparametric approaches to clustering emerged in the 1970's: clustering by level sets or cluster tree as proposed by Hartigan, and clustering by gradient lines or gradient flow as proposed by Fukunaga and Hosteler.
Moving Up the Cluster Tree with the Gradient Flow
The paper establishes a strong correspondence between two important clustering approaches that emerged in the 1970's: clustering by level sets or cluster tree as proposed by Hartigan and clustering by gradient lines or gradient flow as proposed by Fukunaga and Hostetler.
Algorithms for ridge estimation with convergence guarantees
The extraction of filamentary structure from a point cloud is discussed.
Space Partitioning and Regression Mode Seeking via a Mean-Shift-Inspired Algorithm
The mean shift (MS) algorithm is a nonparametric method used to cluster sample points and find the local modes of kernel density estimates, using an idea based on iterative gradient ascent.
