Computational methods for integral equations
Computational methods for integral equations
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
The Effect of the Input Density Distribution on Kernel-based Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Multivariate Density Estimation: an SVM Approach
Multivariate Density Estimation: an SVM Approach
An Expectation-Maximization Approach to Nonlinear Component Analysis
Neural Computation
Minimax mutual information approach for independent component analysis
Neural Computation
Some Equivalences between Kernel Methods and Information Theoretic Methods
Journal of VLSI Signal Processing Systems
Online prediction of time series data with kernels
IEEE Transactions on Signal Processing
PCA based Hurst exponent estimator for fBm signals under disturbances
IEEE Transactions on Signal Processing
Optimized fixed-size kernel models for large data sets
Computational Statistics & Data Analysis
Kernel-based manifold learning for statistical analysis of diffusion tensor images
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Fast support vector regression based on cut
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
Load forecasting using fixed-size least squares support vector machines
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Optimality of kernel density estimation of prior distribution in bayes network
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Spectral element approximation of Fredholm integral eigenvalue problems
Journal of Computational and Applied Mathematics
| Hi-index | 0.01 |
Kernel principal component analysis has been introduced as a method of extracting a set of orthonormal nonlinear features from multivariate data, and many impressive applications are being reported within the literature. This article presents the view that the eigenvalue decomposition of a kernel matrix can also provide the discrete expansion coefficients required for a nonparametric orthogonal series density estimator. In addition to providing novel insights into nonparametric density estimation, this article provides an intuitively appealing interpretation for the nonlinear features extracted from data using kernel principal component analysis.