Fractal dimension and similarity search in high-dimensional spatial databases

被引：0
|
作者：
Malcok, Mehmet [1 ]
Aslandogan, Y. Alp. [2 ]
Yesildirek, Aydin [3 ]
机构：
[1] Behrend Coll, Dept Comp Sci, Erie, PA 16563 USA
[2] Univ Texas Arlington, Dept Comp Sci & Engn, Arlington, TX 76019 USA
[3] Gannan Univ, Dept Elect Engn, Erie, PA 16563 USA
来源：
IRI 2006: PROCEEDINGS OF THE 2006 IEEE INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND INTEGRATION | 2006年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper the relationship between the dimension of the address space and the intrinsic ("fractal") dimension of the data set is investigated An estimate of a lower bound for the number of features needed in a similarity search is given and it is shown that this bound is a function of the intrinsic dimension of the data set. Our result indicates the "deflation" of the dimensionality curse in fractal data sets by showing the explicit relationship between the intrinsic dimension of the data set and the embedding dimension of the address space. More precisely, we show that the relationship between the intrinsic dimension and the embedded dimension is linear
引用
下载
收藏
页码：380 / +
页数：2
相关论文
共 50 条
  • [41] 3-Way-Trees: A similarity search method for high-dimensional descriptor matching
    Valle, Eduardo
    Cord, Matthieu
    Philipp-Foliguet, Sylvie
    2007 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-7, 2007, : 173 - +
  • [42] Fast similarity search in three-dimensional structure databases
    Wang, X
    Wang, JTL
    JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES, 2000, 40 (02): : 442 - 451
  • [43] The spatial arrangement method of measuring similarity can capture high-dimensional semantic structures
    Richie, Russell
    White, Bryan
    Bhatia, Sudeep
    Hout, Michael C.
    BEHAVIOR RESEARCH METHODS, 2020, 52 (05) : 1906 - 1928
  • [44] The spatial arrangement method of measuring similarity can capture high-dimensional semantic structures
    Russell Richie
    Bryan White
    Sudeep Bhatia
    Michael C. Hout
    Behavior Research Methods, 2020, 52 : 1906 - 1928
  • [45] High-Dimensional Simplexes for Supermetric Search
    Connor, Richard
    Vadicamo, Lucia
    Rabitti, Fausto
    SIMILARITY SEARCH AND APPLICATIONS, SISAP 2017, 2017, 10609 : 96 - 109
  • [46] Similarity Query Processing for High-Dimensional Data
    Qin, Jianbin
    Wang, Wei
    Xiao, Chuan
    Zhang, Ying
    PROCEEDINGS OF THE VLDB ENDOWMENT, 2020, 13 (12): : 3437 - 3440
  • [47] Element similarity in high-dimensional materials representations
    Onwuli, Anthony
    Hegde, Ashish V.
    Nguyen, Kevin V. T.
    Butler, Keith T.
    Walsh, Aron
    DIGITAL DISCOVERY, 2023, 2 (05): : 1558 - 1564
  • [48] Similarity Learning for High-Dimensional Sparse Data
    Liu, Kuan
    Bellet, Aurelien
    Sha, Fei
    ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 38, 2015, 38 : 653 - 662
  • [49] Multi-surrogate-assisted stochastic fractal search algorithm for high-dimensional expensive problems *
    Cheng, Xiaodi
    Yu, Yongguang
    Hu, Wei
    INFORMATION SCIENCES, 2023, 640
  • [50] Direct calculation of the f(α) fractal dimension spectrum from high-dimensional correlation-integral partitions
    Potter, M.
    Kinsner, W.
    2007 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL III, PTS 1-3, PROCEEDINGS, 2007, : 989 - +
12345