On some transformations of high dimension, low sample size data for nearest neighbor classification

DOI10.1007/s10994-015-5495-yzbMath1352.62100OpenAlexW1965999538MaRDI QIDQ255361

Subhajit Dutta, Anil Kumar Ghosh

Publication date: 9 March 2016

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-015-5495-y

zbMATH Keywords

law of large numbersmisclassification probabilityreproducing kernel Hilbert spaceBayes risk\(\rho \)-mixing sequencesHDLSS datahigh-dimensional geometryinter-point distances

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Nonparametric estimation (62G05)

Related Items

Graphical Comparison of High‐Dimensional Distributions ⋮ Interpoint Distance Classification of High Dimensional Discrete Observations ⋮ Random forest kernel for high-dimension low sample size classification ⋮ Unnamed Item

Uses Software

• randomForest
• ElemStatLearn

Cites Work

• A nonparametric two-sample test applicable to high dimensional data
• Kernel methods in machine learning
• Choice of neighbor order in nearest-neighbor classification
• PCA consistency in high dimension, low sample size context
• Robust nearest-neighbor methods for classifying high-dimensional data
• Generative models for similarity-based classification
• A distribution-free two-sample run test applicable to high-dimensional data
• 10.1162/15324430260185592
• 10.1162/15324430260185646
• Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties
• Geometric Representation of High Dimension, Low Sample Size Data
• Nearest neighbor pattern classification
• Random forests
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