BI-PLOT TO SHOW CORRELATIONS OF ENVIRONMENTAL VARIABLES IN SELF-ORGANIZING FEATURE MAP ORDINATION

被引：2
|
作者：
Song, Naiqi [1 ]
Yan, Yongmei [2 ]
Li, Junling [2 ]
机构：
[1] Beijing Univ Chinese Med, Sch Chinese Mat Med, Beijing 102488, Peoples R China
[2] Beijing Normal Univ, Coll Life Sci, Minist Educ, Key Lab Biodivers Sci & Ecol Engn, Beijing 100875, Peoples R China
来源：
JP JOURNAL OF BIOSTATISTICS | 2020年 / 17卷 / 02期
基金：
中国国家自然科学基金;
关键词：
direct ordination; bi-plot; self-organizing feature map; plant community; COMMUNITIES;
D O I：
10.17654/BS017020359
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
Self-organizing feature map (SOFM) ordination is a new ordination method based on natural neural networks theory in analysis of community-environment relation. However, it is indirect ordination method and cannot combine environmental data. We combine species data with environmental data to construct a bi-plot of SOFM by use of correlation analysis. This was applied to community-environment analysis in the Taihang Mountain. The results showed that the bi-plot of SOFM described the relationships of samples, communities and species with environmental variables clearly. It provided consistent results with CCA. We can conclude that the bi-plot of SOFM is perfectly useful in vegetation-environment analysis.
引用
收藏
页码：359 / 370
页数：12
相关论文
共 39 条
  • [1] Self-organizing feature map with a momentum term
    Hagiwara, M
    NEUROCOMPUTING, 1996, 10 (01) : 71 - 81
  • [2] Fast self-organizing feature map algorithm
    Su, MC
    Chang, HT
    IEEE TRANSACTIONS ON NEURAL NETWORKS, 2000, 11 (03): : 721 - 733
  • [3] Spectrum Sensor Based on a Self-Organizing Feature Map
    Serackis, Arturas
    Stasionis, Liudas
    NONLINEAR DYNAMICS OF ELECTRONIC SYSTEMS, 2014, 438 : 51 - 58
  • [4] PARALLELIZING THE SELF-ORGANIZING FEATURE MAP ON MULTIPROCESSOR SYSTEMS
    WU, CH
    HODGES, RE
    WANG, CJ
    PARALLEL COMPUTING, 1991, 17 (6-7) : 821 - 832
  • [5] Implementation of the self-organizing feature map on parallel computers
    Demian, V
    Mignot, JC
    COMPUTERS AND ARTIFICIAL INTELLIGENCE, 1996, 15 (01): : 63 - 80
  • [6] Constrained self-organizing feature map to preserve feature extraction topology
    Jorge Azorin-Lopez
    Marcelo Saval-Calvo
    Andres Fuster-Guillo
    Jose Garcia-Rodriguez
    Higinio Mora-Mora
    Neural Computing and Applications, 2017, 28 : 439 - 459
  • [7] Constrained self-organizing feature map to preserve feature extraction topology
    Azorin-Lopez, Jorge
    Saval-Calvo, Marcelo
    Fuster-Guillo, Andres
    Garcia-Rodriguez, Jose
    Mora-Mora, Higinio
    NEURAL COMPUTING & APPLICATIONS, 2017, 28 : S439 - S459
  • [8] Generating fuzzy membership function with self-organizing feature map
    Yang, CC
    Bose, NK
    PATTERN RECOGNITION LETTERS, 2006, 27 (05) : 356 - 365
  • [9] A MULTILAYER SELF-ORGANIZING FEATURE MAP FOR RANGE IMAGE SEGMENTATION
    KOH, J
    SUK, MS
    BHANDARKAR, SM
    NEURAL NETWORKS, 1995, 8 (01) : 67 - 86
  • [10] Detection of Rice-Area Using Self-Organizing Feature Map
    Omatu, Sigeru
    2015 INTERNATIONAL CONFERENCE ON INTELLIGENT INFORMATICS AND BIOMEDICAL SCIENCES (ICIIBMS), 2015, : 44 - 47
1234