A course in density estimation
A course in density estimation
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Covariance Matrix Estimation and Classification With Limited Training Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
On cross validation for model selection
Neural Computation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Bayesian Quadratic Discriminant Analysis
The Journal of Machine Learning Research
Regularization Versus Dimension Reduction, Which Is Better?
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Linear dimensionality reduction using relevance weighted LDA
Pattern Recognition
Rapid and brief communication: An efficient kernel discriminant analysis method
Pattern Recognition
Regularization parameter estimation for feedforward neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Paper: Modeling by shortest data description
Automatica (Journal of IFAC)
The minimum description length principle in coding and modeling
IEEE Transactions on Information Theory
Exact minimax strategies for predictive density estimation, data compression, and model selection
IEEE Transactions on Information Theory
Cluster number selection for a small set of samples using the Bayesian Ying-Yang model
IEEE Transactions on Neural Networks
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
Ensemble-based discriminant learning with boosting for face recognition
IEEE Transactions on Neural Networks
One-class SVM applied to identification of diffractive optical variable image
ASID'09 Proceedings of the 3rd international conference on Anti-Counterfeiting, security, and identification in communication
Speed up image annotation based on LVQ technique with affinity propagation algorithm
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Covariance Matrix Estimation with Multi-Regularization Parameters based on MDL Principle
Neural Processing Letters
| Hi-index | 0.01 |
In classifying high-dimensional patterns such as stellar spectra by a Gaussian classifier, the covariance matrix estimated with a small-number sample set becomes unstable, leading to degraded classification accuracy. In this paper, we investigate the covariance matrix estimation problem for small-number samples with high dimension setting based on minimum description length (MDL) principle. A new covariance matrix estimator is developed, and a formula for fast estimation of regularization parameters is derived. Experiments on spectrum pattern recognition are conducted to investigate the classification accuracy with the developed covariance matrix estimator. Higher classification accuracy results are obtained and demonstrated in our new approach.