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Estimation of gradients and coordinate covariation in classification. (English) Zbl 1222.62078
Summary: We introduce an algorithm that simultaneously estimates a classification function as well as its gradient in the supervised learning framework. The motivation for the algorithm is to find salient variables and estimate how they covary. An efficient implementation with respect to both memory and time is given. The utility of the algorithm is illustrated on simulated data as well as a gene expression data set. An error analysis is given for the convergence of the estimate of the classification function and its gradient to the true classification function and true gradient.
MSC:
62H30Classification and discrimination; cluster analysis (statistics)
68T05Learning and adaptive systems
46N30Applications of functional analysis in probability theory and statistics