Bayesian forecasting and dynamic models (2nd ed.)
Bayesian forecasting and dynamic models (2nd ed.)
Multivariate locally adaptive density estimation
Computational Statistics & Data Analysis
Asymmetric Principal Component and Discriminant Analyses for Pattern Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
2D-LDA: A statistical linear discriminant analysis for image matrix
Pattern Recognition Letters
Push-Pull marginal discriminant analysis for feature extraction
Pattern Recognition Letters
RETRACTED: Application of Bayes linear discriminant functions in image classification
Pattern Recognition Letters
Kernel discriminant analysis for regression problems
Pattern Recognition
Generalizing discriminant analysis using the generalized singular value decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminant Independent Component Analysis
IEEE Transactions on Neural Networks
Algorithms for maximum-likelihood bandwidth selection in kernel density estimators
Pattern Recognition Letters
Computational Statistics & Data Analysis
| Hi-index | 0.10 |
Discriminant analysis using Kernel Density Estimator (KDE) is a common tool for classification, but depends on the choice of the bandwidth or smoothing parameter of kernel. In this paper, we introduce a Bayesian Predictive Kernel Discriminant Analysis (BPKDA) eliminating this dependence by integrating the KDE with respect to an appropriate prior probability distribution for the bandwidth. Keypoints of the method are: (1) the formulation of the classification rule in terms of mixture predictive densities obtained by integrating kernel; (2) use of Independent Components Analysis (ICA) to choose a transform matrix so that transformed components are as independent as possible; and (3) nonparametric estimation of the predictive density by KDE for each independent component. Results on benchmark data sets and simulations show that the performance of BPKDA is competitive with, and in some cases significantly better than, Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Naives Bayes discriminant Analysis with normal distribution (NNBDA).