JSAI International Symposium on Artificial Intelligence

JSAI-isAI 2014: New Frontiers in Artificial Intelligence pp 310-316 | Cite as

Central Point Selection in Dimension Reduction Projection Simple-Map with Binary Quantization

  • Quming Jin
  • Masaya Nakashima
  • Takeshi Shinohara
  • Kouichi Hirata
  • Tetsuji Kuboyama
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9067)

Abstract

A Simple-Map (S-Map, for short), which is one of dimension reduction techniques applicable to any metric space, uses the distances between central points and objects as the coordinate values. S-Map with multiple central points is a projection to multidimensional \(L_\infty \) space. In the previous researches for S-Map, the candidates for central points are randomly selected from data objects in database, and the summation of projective distances between sampled pairs of points is used as the scoring function to be maximized. We can improve the above method to select central points by using local search. The coordinate values of central points obtained after local search tend to be the maximum or the minimum ends of the space. By focusing on this tendency, in this paper, we propose a binary quantization to select central points divided into the maximum values and the minimum values based on whether the coordinate value of an object in database is greater than the threshold or not.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Quming Jin
    • 1
  • Masaya Nakashima
    • 1
    • 3
  • Takeshi Shinohara
    • 1
  • Kouichi Hirata
    • 1
  • Tetsuji Kuboyama
    • 2
  1. 1.Department of Artificial IntelligenceKyushu Institute of TechnologyIizukaJapan
  2. 2.Computer CenterGakushuin UniversityToshimaJapan
  3. 3.Icom Systech Co., Ltd.TokyoJapan

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