Learning nonsparse kernels by self-organizing maps for structured data

Fabio Aiolli, Giovanni Martino, Markus Hagenbuchner, Alessandro Sperduti

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

The development of neural network (NN) models able to encode structured input, and the more recent definition of kernels for structures, makes it possible to directly apply machine learning approaches to generic structured data. However, the effectiveness of a kernel can depend on its sparsity with respect to a specific data set. In fact, the accuracy of a kernel method typically reduces as the kernel sparsity increases. The sparsity problem is particularly common in structured domains involving discrete variables which may take on many different values. In this paper, we explore this issue on two well-known kernels for trees, and propose to face it by recurring to self-organizing maps (SOMs) for structures. Specifically, we show that a suitable combination of the two approaches, obtained by defining a new class of kernels based on the activation map of a SOM for structures, can be effective in avoiding the sparsity problem and results in a system that can be significantly more accurate for categorization tasks on structured data. The effectiveness of the proposed approach is demonstrated experimentally on two relatively large corpora of XML formatted data and a data set of user sessions extracted from website logs.

Original languageEnglish
Article number5290054
Pages (from-to)1938-1949
Number of pages12
JournalIEEE Transactions on Neural Networks
Volume20
Issue number12
DOIs
Publication statusPublished - Dec 2009
Externally publishedYes

    Fingerprint

Keywords

  • Kernel methods
  • Self-organizing maps (SOMs)
  • Structured data
  • Tree kernels

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Software
  • Medicine(all)

Cite this