Pattern recognition based on canonical correlations in a high dimension low sample size context

DOI10.1016/j.jmva.2012.04.011zbMath1259.62055OpenAlexW2073102913MaRDI QIDQ444992

Kanta Naito, Inge Koch, Mitsuru Tamatani

Publication date: 24 August 2012

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2012.04.011

zbMATH Keywords

consistencymisclassificationhigh dimension low sample sizenaive Bayes rule

Mathematics Subject Classification ID

Related Items (2)

High dimensional asymptotics for the naive Hotelling T² statistic in pattern recognition ⋮ Kernel naive Bayes discrimination for high‐dimensional pattern recognition

Cites Work

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• High-dimensional classification using features annealed independence rules
• PCA consistency in high dimension, low sample size context
• Some theory for Fisher's linear discriminant function, `naive Bayes', and some alternatives when there are many more variables than observations
• On the distribution of the largest eigenvalue in principal components analysis
• Prediction of multivariate responses with a selected number of principal components
• Unit canonical correlations and high-dimensional discriminant analysis

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