Auto-adaptive multi-scale Laplacian Pyramids for modeling non-uniform data

Ángela Fernández, Neta Rabin, Dalia Fishelov, José R. Dorronsoro

Research output: Contribution to journalArticlepeer-review


Kernel-based techniques have become a common way for describing the local and global relationships of data samples that are generated in real-world processes. In this research, we focus on a multi-scale kernel based technique named Auto-adaptive Laplacian Pyramids (ALP). This method can be useful for function approximation and interpolation. ALP is an extension of the standard Laplacian Pyramids model that incorporates a modified Leave-One-Out Cross Validation procedure, which makes the method stable and automatic in terms of parameters selection without extra cost. This paper introduces a new algorithm that extends ALP to fit datasets that are non-uniformly distributed. In particular, the optimal stopping criterion will be point-dependent with respect to the local noise level and the sample rate. Experimental results over real datasets highlight the advantages of the proposed multi-scale technique for modeling and learning complex, high dimensional data.

Original languageEnglish
Article number103682
JournalEngineering Applications of Artificial Intelligence
StatePublished - Aug 2020


  • Adaptive stopping
  • Kernel methods
  • Laplacian Pyramids
  • Multi-scale interpolation
  • Non-uniform data
  • Overfitting

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering


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