Intrinsic Dimension Estimation : Relevant Techniques and a Benchmark Framework

@inproceedings{Campadelli2015IntrinsicDE,
  title={Intrinsic Dimension Estimation : Relevant Techniques and a Benchmark Framework},
  author={Paola Campadelli and Elena Casiraghi and Claudio Ceruti and Alessandro Rozza},
  year={2015}
}
When dealing with datasets comprising high-dimensional points, it is usually advantageous to discover some data structure. A fundamental information needed to this aim is the minimum number of parameters required to describe the data while minimizing the information loss. This number, usually called intrinsic dimension, can be interpreted as the dimension of the manifold from which the input data are supposed to be drawn. Due to its usefulness in many theoretical and practical problems, in the… CONTINUE READING

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