Dimensionality reduction for data of unknown cluster structure

DOI10.1016/j.ins.2015.10.009OpenAlexW2153942381MaRDI QIDQ1750610

Ewa Nowakowska, Jacek Koronacki, Stan Lipovetsky

Publication date: 22 May 2018

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1407.7811

zbMATH Keywords

principal component analysisdimensionality reductionGaussian mixture modelFisher's subspace

Mathematics Subject Classification ID

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

Related Items (3)

Clusterability assessment for Gaussian mixture models ⋮ Chernoff information between Gaussian trees ⋮ A graph-based semi-supervised \(k\) nearest-neighbor method for nonlinear manifold distributed data classification

Uses Software

• ElemStatLearn

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Generation of random clusters with specified degree of separation
• Clusterability assessment for Gaussian mixture models
• Generating random correlation matrices based on partial correlations
• PCA and SVD with nonnegative loadings
• Distance preserving linear feature selection
• Linear dimension reduction and Bayes classification
• Linear dimension reduction and Bayes classification with unknown population parameters
• A model for dimension reduction in pattern recognition using continuous data
• Feature extraction and dimensionality reduction algorithms and their applications in vowel recognition.
• Learning mixtures of separated nonspherical Gaussians
• The topography of multivariate normal mixtures
• Efficiently learning mixtures of two Gaussians
• Uncertainty Analysis with High Dimensional Dependence Modelling
• Isotropic PCA and Affine-Invariant Clustering
• Optimal linear feature selection for a general class of statistical pattern recognition models
• Matrix Analysis
• Classification into two Multivariate Normal Distributions with Different Covariance Matrices
• Behavior of the NORTA method for correlated random vector generation as the dimension increases
• Corrigendum
• 10.1162/1532443041827934
• Learning Theory
• Learning Theory
• RELATIONS BETWEEN TWO SETS OF VARIATES

This page was built for publication: Dimensionality reduction for data of unknown cluster structure