Physics – Data Analysis – Statistics and Probability
Scientific paper
2006-02-28
Physics
Data Analysis, Statistics and Probability
17 pages, 5 figures. Revised manuscript to appear in Pattern Recognition
Scientific paper
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.
Bailey Colin
Morozov Vitali
Napoletani Domenico
Sauer Tilman
Struppa Daniele C.
No associations
LandOfFree
Functional dissipation microarrays for classification does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Functional dissipation microarrays for classification, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Functional dissipation microarrays for classification will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-550210