Heterogeneous Datasets Representation and Learning using Diffusion Maps and Laplacian Pyramids

@inproceedings{Rabin2012HeterogeneousDR,
  title={Heterogeneous Datasets Representation and Learning using Diffusion Maps and Laplacian Pyramids},
  author={Neta Rabin and Ronald R. Coifman},
  booktitle={SDM},
  year={2012}
}
The diffusion maps together with the geometric harmonics provide a method for describing the geometry of high dimensional data and for extending these descriptions to new data points and to functions, which are defined on the data. This method suffers from two limitations. First, even though real-life data is often heterogeneous , the assumption in diffusion maps is that the attributes of the processed dataset are comparable. Second, application of the geometric harmonics requires careful… CONTINUE READING
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