Clustering by principal component analysis with Gaussian kernel in high-dimension, low-sample-size settings

DOI10.1016/j.jmva.2021.104779zbMath1479.62044OpenAlexW3167925194MaRDI QIDQ2048123

Kazuyoshi Yata, Makoto Aoshima, Yugo Nakayama

Publication date: 5 August 2021

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

Full work available at URL: https://doi.org/10.1016/j.jmva.2021.104779

zbMATH Keywords

spherical dataHDLSSnonlinear PCAradial basis function kernelPC score

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (2)

Asymptotic properties of hierarchical clustering in high-dimensional settings ⋮ A 50-year personal journey through time with principal component analysis

Uses Software

• SigClust

Cites Work

• Asymptotics of hierarchical clustering for growing dimension
• A distance-based, misclassification rate adjusted classifier for multiclass, high-dimensional data
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• Bias-corrected support vector machine with Gaussian kernel in high-dimension, low-sample-size settings
• Statistical analysis on high-dimensional spheres and shape spaces
• A General Framework For Consistency of Principal Component Analysis
• Statistical Significance of Clustering for High-Dimension, Low–Sample Size Data
• Two-Stage Procedures for High-Dimensional Data
• Two-sample tests for high-dimension, strongly spiked eigenvalue models
• A survey of high dimension low sample size asymptotics
• Geometric consistency of principal component scores for high‐dimensional mixture models and its application
• Geometric Representation of High Dimension, Low Sample Size Data
• When and Why are Principal Component Scores a Good Tool for Visualizing High‐dimensional Data?
• The high-dimension, low-sample-size geometric representation holds under mild conditions
• Clustering high dimension, low sample size data using the maximal data piling distance
• Statistical Significance for Hierarchical Clustering

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