The grand tour: a tool for viewing multidimensional data
SIAM Journal on Scientific and Statistical Computing
Information Theoretic Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Global Optimization
Energy, entropy and information potential for neural computation
Energy, entropy and information potential for neural computation
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Supervised multidimensional scaling for visualization, classification, and bipartite ranking
Computational Statistics & Data Analysis
Differential Evolution Classifier in Noisy Settings and with Interacting Variables
Applied Soft Computing
Simultaneous feature selection and classification using kernel-penalized support vector machines
Information Sciences: an International Journal
Information Sciences: an International Journal
Enhancing the search ability of differential evolution through orthogonal crossover
Information Sciences: an International Journal
Accelerating Differential Evolution Using an Adaptive Local Search
IEEE Transactions on Evolutionary Computation
Optimized distance metrics for differential evolution based nearest prototype classifier
Expert Systems with Applications: An International Journal
Automatic Clustering Using an Improved Differential Evolution Algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Hi-index | 0.07 |
We propose a new method to project n-dimensional data onto two dimensions, for visualization purposes. Our goal is to produce a bi-dimensional representation that better separate existing clusters. Accordingly, to generate this projection we apply Differential Evolution as a meta-heuristic to optimize a divergence measure of the projected data. This divergence measure is based on the Cauchy-Schwartz divergence, extended for multiple classes. It accounts for the separability of the clusters in the projected space using the Renyi entropy and Information Theoretical Clustering analysis. We test the proposed method on two synthetic and five real world data sets, obtaining well separated projected clusters in two dimensions. These results were compared with results generated by PCA and a recent likelihood based visualization method.