Bayesian Classification With Gaussian Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
The nonuniform discrete Fourier transform and its applications in signal processing
The nonuniform discrete Fourier transform and its applications in signal processing
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Advanced Digital Signal Processing: Theory and Applications
Advanced Digital Signal Processing: Theory and Applications
Spiders: a new user interface for rotation and visualization of n-dimensional point sets
VIS '94 Proceedings of the conference on Visualization '94
Learning to learn with the informative vector machine
ICML '04 Proceedings of the twenty-first international conference on Machine learning
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Behavior recognition via sparse spatio-temporal features
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Human action recognition by feature-reduced Gaussian process classification
Pattern Recognition Letters
Robust classifiers for data reduced via random projections
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Incremental Linear Discriminant Analysis for Face Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A New Feature Selection Scheme Using a Data Distribution Factor for Unsupervised Nominal Data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Effective Feature Extraction in High-Dimensional Space
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
| Hi-index | 0.01 |
In the pattern recognition problem, numerous studies have been conducted to choose an appropriate feature space for improved classification results. Instead of directly applying any transformations to the input feature data, we aim to adjust the data distribution by appropriately rotating the coordinate system of the original feature data (in the high dimensional space), so that the data distribution along each feature dimension changes accordingly to support better modeling for classification. In particular, in this paper we present a novel feature space rotation strategy for Gaussian process classification, where rotation is applied to pairwise dimensions at a time and only those rotations that make the data more consistent among different dimensions are kept, as determined by an effective consistency measure we define. In this way, the data are adapted to better fit into the isotropic Gaussian process kernel. Extensive experimental results on various types of data sets have demonstrated the effectiveness of our approach.