Modified Quadratic Discriminant Functions and the Application to Chinese Character Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handwritten Chinese Character Recognition: Alternatives to Nonlinear Normalization
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Using Discriminant Analysis for Multi-class Classification
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Minimum Error Minimax Probability Machine
The Journal of Machine Learning Research
The Journal of Machine Learning Research
A novel kernel-based maximum a posteriori classification method
Neural Networks
Dimensionality Reduction by Minimal Distance Maximization
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Group Lasso Estimation of High-dimensional Covariance Matrices
The Journal of Machine Learning Research
Discriminative learning quadratic discriminant function for handwriting recognition
IEEE Transactions on Neural Networks
Maxi–Min Margin Machine: Learning Large Margin Classifiers Locally and Globally
IEEE Transactions on Neural Networks
| Hi-index | 0.01 |
Multivariate Gaussian distribution is a popular assumption in many pattern recognition tasks. The quadratic discriminant function (QDF) is an effective classification approach based on this assumption. An improved algorithm, called modified QDF (or MQDF in short) has achieved great success and is widely recognized as the state-of-the-art method in character recognition. However, because both of the two approaches estimate the mean and covariance by the maximum-likelihood estimation (MLE), they often lead to the loss of the classification accuracy when the number of the training samples is small. To attack this problem, in this paper, we engage the graphical lasso method to estimate the covariance and propose a new classification method called the graphical lasso quadratic discriminant function (GLQDF). By exploiting a coordinate descent procedure for the lasso, GLQDF can estimate the covariance matrix (and its inverse) more precisely. Experimental results demonstrate that the proposed method can perform better than the competitive methods on two artificial and nine real datasets (including both benchmark digit and Chinese character data).