A solution to the curse of dimensionality problem in pairwise scoring techniques

Man Wai Mak, Sun Yuan Kung

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

14 Citations (Scopus)


This paper provides a solution to the curse of dimensionality problem in the pairwise scoring techniques that are commonly used in bioinformatics and biometrics applications. It has been recently discovered that stacking the pairwise comparison scores between an unknown patterns and a set of known patterns can result in feature vectors with nice discriminative properties for classification. However, such technique can lead to curse of dimensionality because the vectors size is equal to the training set size. To overcome this problem, this paper shows that the pairwise score matrices possess a symmetric and diagonally dominant property that allows us to select the most relevant features independently by an FDA-like technique. Then, the paper demonstrates the capability of the technique via a protein sequence classification problem. It was found that 10-fold reduction in the number of feature dimensions and recognition time can be achieved with just 4% reduction in recognition accuracy.
Original languageEnglish
Title of host publicationNeural Information Processing - 13th International Conference, ICONIP 2006, Proceedings
PublisherSpringer Verlag
Number of pages10
ISBN (Print)3540464794, 9783540464792
Publication statusPublished - 1 Jan 2006
Event13th International Conference on Neural Information Processing, ICONIP 2006 - Hong Kong, Hong Kong
Duration: 3 Oct 20066 Oct 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4232 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference13th International Conference on Neural Information Processing, ICONIP 2006
Country/TerritoryHong Kong
CityHong Kong


  • Curse of dimensionality
  • Feature selection
  • Fisher discriminant analysis
  • Protein sequence analysis
  • Subcellular localization
  • Support vector machines

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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