Classification of hyperdimensional data using data fusion approaches

Statistical classification methods based on consensus from several data sources are considered with respect to classification and feature extraction of hyperdimensional data. The consensus theoretic methods need weighting mechanisms to control the influence of each data source in the combined classification. The weights are optimized in order to improve the combined classification accuracies. Decision boundary feature extraction is considered as a preprocessing method in the data fusion. Consensus theory optimized with neural networks outperforms all other methods in terms of test accuracies in the experiments.