Corpus ID: 18866487

Principal Component Analysis With Missing Data and Outliers

@inproceedings{ChenPrincipalCA,
  title={Principal Component Analysis With Missing Data and Outliers},
  author={H. Chen}
}
Principal component analysis (PCA) [10] is a well established technique for dimensionality reduction, and a chapter on the subject may be found in numerous texts on multivariate analysis. Examples of its many applications include data compression, image processing, visualisation, exploratory data analysis, pattern recognition and time series prediction. The popularity of PCA comes from three important properties. First, it is the optimal (in terms of mean squared error) linear scheme for… CONTINUE READING
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