The Linear Factorial Smoothing for the Analysis of Incomplete Data

  • Basavanneppa Tallur
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3776)


Huge amounts of data are generated in every field of science and technology and the need for the proper data analysis tools and their adaptation to the ever-increasing data size is more and more crucial. Statistical exploratary data analysis techniques –such as principal component analysis, correspondence analysis, clustering and classification among others– are greatly useful in discovering useful information –or knowledge– hidden in data but they require the data set to be complete. In many situations the data is incomplete for various reasons. Erroneous and uncertain data may also be considered as missing since their use may lead to incorrect results. Many research works have addressed this issue in specific applications. This paper presents a simple and efficient iterative method for estimating the missing values in the data set based on linear factorial smoothing. Though this work was prompted by the recurrent problem faced in the field of bioinformatics while analysing the gene expression data, the method proposed for missing value imputation in this paper may be useful in any area.


Principal Component Analysis Down Syndrome Gene Expression Data Correspondence Analysis Independent Component Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Basavanneppa Tallur
    • 1
  1. 1.IRISA, Université de Rennes 1RennesFrance

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