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We present a Bayesian model selection approach to estimate the intrinsic dimensionality of a high-dimensional dataset.
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Determining the number of factors in approximate factor models
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I. T. Jolliffe · 2002
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A. Hannachi, I. T. Jolliffe, D. B. Stephenson, and N. Trendafilov · 2006
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Automatic dimensionality selection from the scree plot via the use of profile likelihood
M. Zhu and A. Ghodsi · 2006
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High-dimensional data clustering
C. Bouveyron, S. Girard, and C. Schmid · 2007
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P. D. Hoff · 2007
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Nonparametric Bayesian sparse factor models with application to gene expression modeling
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Selecting the number of components in principal component analysis using cross-validation approximations
J. Josse and F. Husson · 2012
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Multivariate generalized Laplace distribution and related random fields
T. Kozubowski, K. Podgórski, and I. Rychlik · 2013
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What is principal component analysis?
M. Ringnér · 2008
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Dimension estimation in noisy PCA with SURE and random matrix theory
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The optimal hard threshold for singular values is 4/ 3 \sqrt{3}
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Condition for perfect dimensionality recovery by variational Bayesian PCA
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Principal component analysis: a review and recent developments
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On the Gaussian mixture representation of the Laplace distribution
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