Constrained variable clustering and the best basis problem in functional data analysis

Details Constrained variable clustering and the best basis problem in functional data analysis Constrained variable clustering and the best basis problem in functional data analysis

2012-01-04

arxiv.org/abs/1201.0959v1

Classification and Multivariate Analysis for Complex Data Structures 435-444 (2011)

Statistics

Machine Learning

Scientific paper

10.1007/978-3-642-13312-1_46

Functional data analysis involves data described by regular functions rather than by a finite number of real valued variables. While some robust data analysis methods can be applied directly to the very high dimensional vectors obtained from a fine grid sampling of functional data, all methods benefit from a prior simplification of the functions that reduces the redundancy induced by the regularity. In this paper we propose to use a clustering approach that targets variables rather than individual to design a piecewise constant representation of a set of functions. The contiguity constraint induced by the functional nature of the variables allows a polynomial complexity algorithm to give the optimal solution.

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