Kernel naive Bayes discrimination for high‐dimensional pattern recognition

DOI10.1111/ANZS.12279zbMath1440.62247OpenAlexW2995218026WikidataQ126523874 ScholiaQ126523874MaRDI QIDQ5117650

Hiroaki Tanaka, Inge Koch, Kanta Naito

Publication date: 26 August 2020

Published in: Australian & New Zealand Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/anzs.12279

zbMATH Keywords

naive BayesHDLSS asymptoticskernel discriminant function

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Pattern recognition, speech recognition (68T10)

Cites Work

• Unnamed Item
• Pattern recognition based on canonical correlations in a high dimension low sample size context
• 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
• A direct LDA algorithm for high-dimensional data -- with application to face recognition

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