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Learning nonsparse kernels by self-organizing maps for structured data.

Fabio Aiolli1, Giovanni Da San Martino, Markus Hagenbuchner

  • 1Department of Pure and Applied Mathematics, University of Padova, Padova, Italy. aiolli@math.unipd.it

IEEE Transactions on Neural Networks
|October 23, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a new machine learning approach using self-organizing maps (SOMs) for structures to overcome kernel sparsity issues in structured data analysis, significantly improving categorization accuracy.

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Area of Science:

  • Machine Learning
  • Data Mining
  • Artificial Intelligence

Background:

  • Machine learning models can encode structured input, but kernel effectiveness depends on data sparsity.
  • Kernel sparsity, common in discrete structured domains, reduces accuracy in kernel methods.
  • Existing tree kernels face challenges with sparse structured data.

Purpose of the Study:

  • To address the kernel sparsity problem in machine learning for structured data.
  • To propose a novel approach combining kernels for structures with self-organizing maps (SOMs).
  • To enhance categorization accuracy on structured datasets.

Main Methods:

  • Explored sparsity issues with two established tree kernels.
  • Proposed a new class of kernels based on the activation map of SOMs for structures.
  • Applied the combined approach to categorization tasks on structured data.

Main Results:

  • The proposed SOM-based kernels effectively mitigate the sparsity problem.
  • The new approach significantly improved accuracy in categorization tasks.
  • Demonstrated effectiveness on large XML corpora and website log user session data.

Conclusions:

  • Combining kernels for structures with SOMs offers a robust solution to sparsity.
  • This method enhances the performance of machine learning on structured data.
  • The approach is validated by experimental results on diverse structured datasets.