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Independent Component Analysis for the objective classification of globular clusters of the galaxy NGC 5128

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  • Chattopadhyay, Asis Kumar
  • Mondal, Saptarshi
  • Chattopadhyay, Tanuka

Abstract

Independent Component Analysis (ICA) is closely related to Principal Component Analysis (PCA) and factor analysis. Whereas ICA finds a set of source data that are mutually independent, PCA finds a set of data that are mutually uncorrelated. The assumption that data from different physical processes are uncorrelated does not always imply the reverse case that uncorrelated data are coming from different physical processes. This is because lack of correlation is a weaker property than independence.

Suggested Citation

  • Chattopadhyay, Asis Kumar & Mondal, Saptarshi & Chattopadhyay, Tanuka, 2013. "Independent Component Analysis for the objective classification of globular clusters of the galaxy NGC 5128," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 17-32.
    • Handle: RePEc:eee:csdana:v:57:y:2013:i:1:p:17-32
    DOI: 10.1016/j.csda.2012.06.008
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    References listed on IDEAS

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    1. Sugar, Catherine A. & James, Gareth M., 2003. "Finding the Number of Clusters in a Dataset: An Information-Theoretic Approach," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 750-763, January.
    2. Salibian-Barrera, Matias & Van Aelst, Stefan & Willems, Gert, 2006. "Principal Components Analysis Based on Multivariate MM Estimators With Fast and Robust Bootstrap," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1198-1211, September.
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    Cited by:

    1. Secchi, Piercesare & Vantini, Simone & Zanini, Paolo, 2016. "Hierarchical independent component analysis: A multi-resolution non-orthogonal data-driven basis," Computational Statistics & Data Analysis, Elsevier, vol. 95(C), pages 133-149.

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