Dissipative Functional Microarrays for Classification

摘要

The invention provides for a technique of extracting information from signals by allowing a user to select a classifier and a figure of merit for quantifying classification quality; select a transform to generate features from input data; use a recursive process of functional dissipation to generate dissipative features from features that are generated according to the transform; search for one or more dissipative features that maximize the figure of merit on a training set; and classify a test set with the classifier by using one or more of the dissipative features. Functional dissipation uses the transforms recursively by generating random masking functions and extracting features with one or more generalized matching pursuit iterations. In each iteration, the recursive process may modify several features of the transformed signal with the largest absolute values according to a specific masking function.