Application of Interactive Regularized Discriminant Analysis to Wine Data

被引:0
|
作者
Romisch, Ute [1 ]
Vandev, Dimitar [2 ]
Zur, Katrin [1 ]
机构
[1] Tech Univ Berlin, Dept Informat, Fac Proc Engn, Gustav Meyer Allee 25, D-13355 Berlin, Germany
[2] St Kl Ohridski Univ, Sofia, Bulgaria
关键词
Regularization; Classification;
D O I
暂无
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
Testing the possibility of determining the geographical origin (country) of wines on the base of chemico-analytical parameters was the aim of the European project "Establishing of a wine data bank for analytical parameters for wines from Third countries (G6RD-CT-2001-00646-WINE DB)" supported by the European Commission. Therefore a data base containing 400 samples of commercial and authentic wines from Hungary, Czech Republic, Romania and South Africa was created. For each of those samples around 100 analytical parameters, among them rare earth elements and isotopic ratios were measured. Besides other multivariate statistical methods of discrimination and classification the method of regularized discriminant analysis (RDA) was used to distinguish the wines of the different countries on the base of a minimal number of the most important parameters. A MATLAB-program, developed by Vandev (2004) which allows an interactive stepwise discriminant model building on the base of an optimal choice of the "nonlinearity" parameter alpha was used. This program will be described shortly and models for commercial wines with corresponding classification and prediction error rates will be given. As a result of using RDA it was possible to reduce the number of analytical parameters to the eight to infer the geographical origin of these commercial wines.
引用
收藏
页码:45 / 55
页数:11
相关论文
共 50 条
  • [21] Regularized Nonlinear Discriminant Analysis An Approach to Robust Dimensionality Reduction for Data Visualization
    Becker, Martin
    Lippel, Jens
    Stuhlsatz, Andre
    PROCEEDINGS OF THE 12TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS (VISIGRAPP 2017), VOL 3, 2017, : 116 - 127
  • [22] Shrunken centroids regularized discriminant analysis as a promising strategy for metabolomics data exploration
    Chen, Chen
    Zhang, Zhi-Min
    Ouyang, Mei-Lan
    Liu, Xinbo
    Yi, Lunzhao
    Liang, Yi-Zeng
    Zhang, Chao-Ping
    JOURNAL OF CHEMOMETRICS, 2015, 29 (03) : 154 - 164
  • [23] A study of factors affecting wine volatile composition and its application in discriminant analysis
    Ferreira, V
    Fernandez, P
    Cacho, JF
    FOOD SCIENCE AND TECHNOLOGY-LEBENSMITTEL-WISSENSCHAFT & TECHNOLOGIE, 1996, 29 (03): : 251 - 259
  • [24] Regularized coplanar discriminant analysis for dimensionality reduction
    Huang, Ke-Kun
    Dai, Dao-Qing
    Ren, Chuan-Xian
    PATTERN RECOGNITION, 2017, 62 : 87 - 98
  • [25] On the dimension effect of regularized linear discriminant analysis
    Wang, Cheng
    Jiang, Binyan
    ELECTRONIC JOURNAL OF STATISTICS, 2018, 12 (02): : 2709 - 2742
  • [26] A deterministic approach to regularized linear discriminant analysis
    Sharma, Alok
    Paliwal, Kuldip K.
    NEUROCOMPUTING, 2015, 151 : 207 - 214
  • [28] Regularized orthogonal local fisher discriminant analysis
    Xu, Shuhua
    Journal of Digital Information Management, 2013, 11 (02): : 154 - 159
  • [29] Structure Regularized Unsupervised Discriminant Feature Analysis
    Fan, Mingyu
    Chang, Xiaojun
    Tao, Dacheng
    THIRTY-FIRST AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2017, : 1870 - 1876
  • [30] A Large Dimensional Study of Regularized Discriminant Analysis
    Elkhalil, Khalil
    Kammoun, Abla
    Couillet, Romain
    Al-Naffouri, Tareq Y.
    Alouini, Mohamed-Slim
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2020, 68 : 2464 - 2479