Journal of KIISE:Databases (한국정보과학회논문지:데이타베이스)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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Indexing and Searching for Reduced-Dimensional Vectors

차원 축소 벡터들을 위한 인덱싱 및 검색

  • 정승도 (한양사이버대하교 정보통신공학과) ;
  • 김상욱 (한양대학교 컴퓨터공학부) ;
  • 최병욱 (한양대학교 컴퓨터공학부)
  • Published : 2010.02.15
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Abstract

In this paper, we first address the problems associated with indexing and searching for reduced-dimensional vectors, which are reduced by using a combination of angle approximation and dimension grouping. Then, we propose a novel method to solve the problems. We also show the superiority of the proposed method by performing extensive experiments with synthetic and real-life data sets.

본 논문에서는 각도 성분 근사와 차원 그룹화 기법을 이용한 차원 축소 기법에 의해 변환된 축소 데이터를 색인하고 검색하기 위해서 해결되어야 하는 문제들을 분석하고 이를 해결하기 위한 방법을 제안한다. 또한 다양한 실험에 의한 성능 평가를 통하여 제안하는 방법의 우수성을 규명한다.

Keywords

References

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