GTM: the generative topographic mapping
Neural Computation
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Relationship-Based Clustering and Visualization for High-Dimensional Data Mining
INFORMS Journal on Computing
Hierarchical Gaussian process latent variable models
Proceedings of the 24th international conference on Machine learning
A Nonlinear Feature Extraction Algorithm Using Distance Transformation
IEEE Transactions on Computers
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Cluster-based visualisation with scatter matrices
Pattern Recognition Letters
Kernel maximum scatter difference based feature extraction and its application to face recognition
Pattern Recognition Letters
Kernel Discriminant Analysis for Positive Definite and Indefinite Kernels
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear Dimensionality Reduction
Nonlinear Dimensionality Reduction
Artificial neural networks for feature extraction and multivariate data projection
IEEE Transactions on Neural Networks
| Hi-index | 0.01 |
Visualisation with good discrimination between data cohorts is important for exploratory data analysis and for decision support interfaces. This paper proposes a kernel extension of the cluster-based linear visualisation method described in Lisboa et al. [15]. A representation of the data in dual form permits the application of the kernel trick, so projecting the data onto the orthonormalised cohort means in the feature space. The only parameters of the method are those for the kernel function. The method is shown to obtain well-discriminating visualisations of non-linearly separable data with low computational cost. The linearity of the visualisation was tested using nearest neighbour and linear discriminant classifiers, achieving significant improvements in classification accuracy with respect to the original features, especially for high-dimensional data, where 93% accuracy was obtained for the Splice-junction Gene Sequences data set from the UCI repository.