Classification through hierarchical clustering and dimensionality reduction

This work describes a two-mode clustering hierarchical model capable of dealing with high dimensional data spaces. The algorithm seeks a transformed subspace which can represent the initial data, simplify the problem and possibly lead to a better categorization level. We test the algorithm on two hard classification problems, the phoneme and the pedestrian recognition; both are typical classification problems from real-life applications. Finally, the model is compared with many other algorithms.