Regular Article: Randomized Nonlinear Projections Uncover High-Dimensional Structure
Advances in Applied Mathematics
Olfactory Classification via Interpoint Distance Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Nearest-Neighbor Search in Dissimilarity Spaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dissimilarity representations allow for building good classifiers
Pattern Recognition Letters
Dissimilarity-based classification of spectra: computational issues
Real-Time Imaging - Special issue on spectral imaging
A Triangulation Method for the Sequential Mapping of Points from N-Space to Two-Space
IEEE Transactions on Computers
A Projection Pursuit Algorithm for Exploratory Data Analysis
IEEE Transactions on Computers
Discriminative components of data
IEEE Transactions on Neural Networks
Class proximity measures - Dissimilarity-based classification and display of high-dimensional data
Journal of Biomedical Informatics
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Previously, we introduced a distance (similarity)-based mapping for the visualization of high-dimensional patterns and their relative relationships. The mapping preserves exactly the original distances from all points to any two reference patterns in a special two-dimensional coordinate system, the relative distance plane (RDP). We extend the RDP mapping's applicability from visualization to classification. Several of the classifiers use the RDP directly. These include the standard linear discriminant analysis (LDA), nearest neighbor classifiers, and a transvariation probabilities-based classification method that is natural in the RDP. Several reference directions can also be combined to create new coordinate systems in which arbitrary classifiers can be developed. We obtain increased confidence in the classification results by cycling through all possible reference pairs and computing a misclassification-based weighted accuracy. The classification results on several high-dimensional biomedical datasets are compared.