Mixtures of probabilistic principal component analyzers
Neural Computation
A comparative investigation on subspace dimension determination
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Eigenvalues of large sample covariance matrices of spiked population models
Journal of Multivariate Analysis
Theoretical Analysis and Comparison of Several Criteria on Linear Model Dimension Reduction
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
IEEE Transactions on Signal Processing
Dimensionality reduction by minimizing nearest-neighbor classification error
Pattern Recognition Letters
An empirical evaluation on dimensionality reduction schemes for dissimilarity-based classifications
Pattern Recognition Letters
Detection of signals by information theoretic criteria: generalasymptotic performance analysis
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing - Part I
Estimation of the Number of Sources in Unbalanced Arrays via Information Theoretic Criteria
IEEE Transactions on Signal Processing
Analysis of the performance and sensitivity ofeigendecomposition-based detectors
IEEE Transactions on Signal Processing
On the behavior of information theoretic criteria for model orderselection
IEEE Transactions on Signal Processing
Paper: Modeling by shortest data description
Automatica (Journal of IFAC)
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Based on the problem of determining the hidden dimensionality (or the number of latent factors) of Factor Analysis (FA) model, this paper provides a theoretic comparison on several classical model selection criteria, including Akaike's Information Criterion (AIC), Bozdogan's Consistent Akaike's Information Criterion (CAIC), Hannan-Quinn information criterion (HQC), Schwarz's Bayesian Information Criterion (BIC). We focus on building up a partial order of the relative underestimation tendency. The order is shown to be AIC, HQC, BIC, and CAIC, indicating the underestimation probabilities from small to large. This order indicates an order of model selection performances to great extent, because underestimations usually take the major proportion of wrong selections when the sample size and the population signal-to-noise ratio (SNR, defined as the ratio of the smallest variance of the hidden dimensions to the variance of noise) decrease. Synthetic experiments by varying the values of the SNR and the training sample size N verify the theoretical results.