Classification by Thresholding
Copyright policy )Classification by Thresholding
A procedure is given which substantially reduces the processing time needed to perform maximum likelihood classification on large data sets. The given method uses a set of fixed thresholds which, if exceeded by one probability density function, makes it unnecessary to evaluate a competing density function. Proofs are given of the existence and optimality of these thresholds for the class of continuous, unimodal, and quasi-concave density functions (which includes the multivariate normal), and a method for computing the thresholds is provided for the specifilc case of multivariate normal densities. An example with remote sensing data consisting of some 20 000 observations of four-dimensional data from nine ground-cover classes shows that by using thresholds, one could cut the processing time almost in half.
- National Aeronautics and Space Administration United States
- Johnson Space Center United States
Classification and discrimination; cluster analysis (statistical aspects), continuous densities, existence, thresholding, reduction in processing time, Nonparametric inference, optimality, multivariate normal densities, remote sensing, maximum likelihood classification, probability of misclassification, quasi-concave density functions, unimodal densities
Classification and discrimination; cluster analysis (statistical aspects), continuous densities, existence, thresholding, reduction in processing time, Nonparametric inference, optimality, multivariate normal densities, remote sensing, maximum likelihood classification, probability of misclassification, quasi-concave density functions, unimodal densities
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).13 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!