EM algorithms for PCA and SPCA
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Independent Component Analysis: Principles and Practice
Independent Component Analysis: Principles and Practice
Biologically valid linear factor models of gene expression
Bioinformatics
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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.