Functional Dissipation Microarrays for Classification
In this article, we describe a new method of extracting information from signals, called functional dissipation, that proves to be very effective for enhancing classification of high resolution, texture-rich data. Our algorithm bypasses to some extent the need to have very specialized feature extraction techniques, and can potentially be used as an intermediate, feature enhancement step in any classification scheme. Functional dissipation is based on signal transforms, but uses the transforms recursively to uncover new features. We generate a variety of masking functions and ‘extract’ features with several generalized matching pursuit iterations. In each iteration, the recursive process modifies several coefficients of the transformed signal with the largest absolute values according to the specific masking function; in this way the greedy pursuit is turned into a slow, controlled, dissipation of the structure of the signal that for some masking functions, enhances separation among classes.
Our case study in this paper is the classification of crystallization patterns of amino acids solutions affected by the addition of small quantities of proteins.
Napoletani, D., Struppa, D. C., Sauer, T., Morozov, V., Vsevolodov, N., & Bailey, C. (2007). Functional dissipation microarrays for classification. Pattern Recognition, 40(12), 3393-3400. doi: 10.1016/j.patcog.2007.03.031