Novel high intrinsic dimensionality estimators

@article{Rozza2012NovelHI,
  title={Novel high intrinsic dimensionality estimators},
  author={Alessandro Rozza and Gabriele Lombardi and Claudio Ceruti and Elena Casiraghi and Paola Campadelli},
  journal={Machine Learning},
  year={2012},
  volume={89},
  pages={37-65}
}
Recently, a great deal of research work has been devoted to the development of algorithms to estimate the intrinsic dimensionality (id) of a given dataset, that is the minimum number of parameters needed to represent the data without information loss. id estimation is important for the following reasons: the capacity and the generalization capability of discriminant methods depend on it; id is a necessary information for any dimensionality reduction technique; in neural network design the… CONTINUE READING

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