Principal component analysis: Most favourite tool in chemometrics

被引：0
|
作者：
Kumar K. [1 ]
机构：
[1] Institute for Wine Analysis and Beverage Research, Hochschule, Geisenheim University, Geisenheim
来源：
Resonance | 2017年 / 22卷 / 8期
关键词：
Chemometrics; chromatography; classification; pattern recognition; principal component analysis;
D O I：
10.1007/s12045-017-0523-9
中图分类号：
学科分类号：
摘要：
Principal component analysis (PCA) is the most commonly used chemometric technique. It is an unsupervised pattern recognition technique. PCA has found applications in chemistry, biology, medicine and economics. The present work attempts to understand how PCA work and how can we interpret its results. © 2017, Indian Academy of Sciences.
引用
收藏
页码：747 / 759
页数：12
相关论文
共 50 条
  • [21] Principal component analysis
    School of Behavioral and Brain Sciences, University of Texas at Dallas, MS: GR4.1, Richardson, TX 75080-3021, United States
    不详
    Wiley Interdiscip. Rev. Comput. Stat., 4 (433-459):
  • [22] Principal component analysis
    Abdi, Herve
    Williams, Lynne J.
    WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS, 2010, 2 (04): : 433 - 459
  • [23] PRINCIPAL COMPONENT ANALYSIS
    WOLD, S
    ESBENSEN, K
    GELADI, P
    CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 1987, 2 (1-3) : 37 - 52
  • [24] Principal component analysis
    Hess, Aaron S.
    Hess, John R.
    TRANSFUSION, 2018, 58 (07) : 1580 - 1582
  • [25] PRINCIPAL COMPONENT ANALYSIS
    ARIES, RE
    LIDIARD, DP
    SPRAGG, RA
    CHEMISTRY IN BRITAIN, 1991, 27 (09) : 821 - 824
  • [26] The Clause of Nations most Favourite
    Penfield, Walter Scott
    AMERICAN JOURNAL OF INTERNATIONAL LAW, 1927, 21 (01) : 203 - 204
  • [27] Segmented principal component transform-principal component analysis
    Barros, AS
    Rutledge, DN
    CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2005, 78 (1-2) : 125 - 137
  • [28] Degrees of freedom estimation in Principal Component Analysis and Consensus Principal Component Analysis
    Hassani, Sahar
    Martens, Harald
    Qannari, El Mostafa
    Kohler, Achim
    CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2012, 118 : 246 - 259
  • [29] Modifications of Most Expressive Feature Reordering Criteria for Supervised Kernel Principal Component Analysis
    Adamiak, Krzysztof
    Duch, Piotr
    Zurek, Dominik
    Slot, Krzysztof
    2015 IEEE 2ND INTERNATIONAL CONFERENCE ON CYBERNETICS (CYBCONF), 2015, : 507 - 511
  • [30] Network principal component analysis: a versatile tool for the investigation of multigroup and multiblock datasets
    Codesido, Santiago
    Hanafi, Mohamed
    Gagnebin, Yoric
    Gonzalez-Ruiz, Victor
    Rudaz, Serge
    Boccard, Julien
    BIOINFORMATICS, 2021, 37 (09) : 1297 - 1303
12345