In this paper, data dimensionality estimation methods are reviewed. The estimation of the dimensionality of a data set is a classical problem of pattern recognition. There are some good reviews (Algorithms for Clustering Data, Prentice-Hall, Englewood Cliffs, NJ, 1988) in literature but they do not include more recent developments based on fractal techniques and neural autoassociators. The aim of this paper is to provide an up-to-date survey of the dimensionality estimation methods of a data set, paying special attention to the fractal-based methods.
Data Dimensionality Estimation Methods: A Survey
CAMASTRA, Francesco
2003-01-01
Abstract
In this paper, data dimensionality estimation methods are reviewed. The estimation of the dimensionality of a data set is a classical problem of pattern recognition. There are some good reviews (Algorithms for Clustering Data, Prentice-Hall, Englewood Cliffs, NJ, 1988) in literature but they do not include more recent developments based on fractal techniques and neural autoassociators. The aim of this paper is to provide an up-to-date survey of the dimensionality estimation methods of a data set, paying special attention to the fractal-based methods.File in questo prodotto:
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