Novel high intrinsic dimensionality estimators

  title={Novel high intrinsic dimensionality estimators},
  author={Alessandro Rozza and Gabriele Lombardi and Claudio Ceruti and Elena Casiraghi and Paola Campadelli},
  journal={Machine Learning},
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


Publications citing this paper.
Showing 1-10 of 17 extracted citations


Publications referenced by this paper.
Showing 1-10 of 57 references

Neural networks for pattern recognition

  • C. M. mathematics. Bishop
  • Bayesian PCA. Advances in Neural Information…
  • 1995
Highly Influential
7 Excerpts

Monte Carlo: concepts, algorithms, and applications. Springer series in operations research

  • G. S. Fishman
  • 1996
Highly Influential
3 Excerpts

Measuring the strangeness of strange attractors

  • P. Grassberger, I. Procaccia
  • Physica D: Nonlinear Phenomena,
  • 1983
Highly Influential
4 Excerpts

Similar Papers

Loading similar papers…