The reported work proposes a new algorithm for classification tasks, an algorithm based on independent directions of the sample data. The classes are learned by the algorithm using the information contained by samples randomly generated from them. The learning process is based on the set of class skeletons, where the class skeleton is represented by the independent axes estimated from data. Basically, for each new sample, the recognition algorithm classifies it in the class whose skeleton is the "nearest" to this example. Comparative analysis is performed and experimentally derived conclusions concerning the performance of the proposed method are reported in the final section of the paper for signals recognition applications.
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