Bimodal Projection-based Features for Pattern Classification

Classification tasks involving high dimensional vectors are affected by the curse of dimensionality requiring large amount of training data. This is because a high-dimensional space with a modest number of samples is mostly empty. To overcome this we employ the principle of Projection Pursuit. The principle is motivated by the aim to search for clusters in high-dimensional space. Data points are projected onto an appropriate projection direction. Search for clusters is in this single dimensional projection space. As a result inherent sparsity of the high-dimensional space is avoided. Classical discriminant analysis methods also seek clusters but require class labels to be specified. One such technique, the Fisher´s linear discriminant (FLD) method, has been used to arrive at an unsupervised algorithm that seeks bimodal projection directions.