• DocumentCode
    578076
  • Title

    Equivalence of MSE and MaxEnt as objective function of PCA

  • Author

    Li, Hong-bao ; Hong-Bao Liu ; Ma, Ning-hua

  • Author_Institution
    Dept. of Educ. Adm., Hebei Univ., Baoding, China
  • Volume
    1
  • fYear
    2012
  • fDate
    15-17 July 2012
  • Firstpage
    152
  • Lastpage
    156
  • Abstract
    Compare to MSE as PCA objective function to minimize, MaxEnt can at least achieve the same performance. In this paper, the authors proved that the two objective functions are equivalence in the sense that they achieve the optimal points in the same direction of principal components. Referring to the dimensionality reduction for the DCI benchmarking dataset, numerical experiments illustrate the equivalence of the two objective functions.
  • Keywords
    learning (artificial intelligence); maximum entropy methods; mean square error methods; principal component analysis; MSE; MaxEnt; PCA; UCI benchmarking dataset; dimensionality reduction; mean squared error minimisation; objective function; principal component analysis; Abstracts; Benchmark testing; Entropy; Face; Face recognition; KPCA; Maximum entropy; Objective function; PCA; Reconstruction error;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics (ICMLC), 2012 International Conference on
  • Conference_Location
    Xian
  • ISSN
    2160-133X
  • Print_ISBN
    978-1-4673-1484-8
  • Type

    conf

  • DOI
    10.1109/ICMLC.2012.6358903
  • Filename
    6358903
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=578076