Characterizing the scale dimension of a high-dimensional classification problem

DOI10.1016/S0031-3203(02)00042-0zbMath1027.68690OpenAlexW2163537809MaRDI QIDQ1856632

David J. Marchette, Carey E. Priebe

Publication date: 11 February 2003

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0031-3203(02)00042-0

zbMATH Keywords

classificationrandom graphhigh-dimensional dataexploratory data analysisinterpoint distanceclass coverartificial nosereduced kernel estimatorscale dimension

Mathematics Subject Classification ID

Pattern recognition, speech recognition (68T10) Computing methodologies and applications (68U99)

Related Items (14)

Comparison of relative density of two random geometric digraph families in testing spatial clustering ⋮ A general SLLN for the one-dimensional class cover problem ⋮ Class cover catch digraphs for latent class discovery in gene expression monitoring by DNA microarrays ⋮ Relative density of the random \(R\)-factor proximity catch digraph for testing spatial patterns of segregation and association ⋮ Spatial Clustering Tests Based on the Domination Number of a New Random Digraph Family ⋮ The distribution of the relative arc density of a family of interval catch digraph based on uniform data ⋮ An investigation of new graph invariants related to the domination number of random proximity catch digraphs ⋮ Extension of one-dimensional proximity regions to higher dimensions ⋮ A new family of proximity graphs: class cover catch digraphs ⋮ The distribution of the domination number of class cover catch digraphs for non-uniform one-dimensional data ⋮ A CLT for a one-dimensional class cover problem ⋮ The use of domination number of a random proximity catch digraph for testing spatial patterns of segregation and association ⋮ A new family of random graphs for testing spatial segregation ⋮ Law of large numbers for a two-dimensional class cover problem

Cites Work

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• Analysis of a greedy heuristic for finding small dominating sets in graphs
• Graph theory applications
• Approximation algorithms for the class cover problem
• Classification by pairwise coupling
• Reducing multidimensional two-sample data to one-dimensional interpoint comparisons
• A digraph represented by a family of boxes or spheres
• Printer graphics for clustering

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