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Robust principal component analysis for functional data

N Locantore JS Marron DG Simpson N Tripoli JT Zhang KL Cohen Graciela Boente Ricardo Fraiman Babette Brumback Christophe Croux Jianqing Fan Alois Kneip John I Marden Daniel Pea Javier Prieto Jim O Ramsay Mariano J Valderrama Ana M Aguilera

Statistics Theory and Methods mathscidoc:1912.43272

Test, 8, (1), 1-73, 1999.6
A method for exploring the structure of populations of complex objects, such as images, is considered. The objects are summarized by feature vectors. The statistical backbone is Principal Component Analysis in the space of feature vectors. Visual insights come from representing the results in the original data space. In an ophthalmological example, endemic outliers motivate the development of a bounded influence approach to PCA.
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@inproceedings{n1999robust,
  title={Robust principal component analysis for functional data},
  author={N Locantore, JS Marron, DG Simpson, N Tripoli, JT Zhang, KL Cohen, Graciela Boente, Ricardo Fraiman, Babette Brumback, Christophe Croux, Jianqing Fan, Alois Kneip, John I Marden, Daniel Pea, Javier Prieto, Jim O Ramsay, Mariano J Valderrama, and Ana M Aguilera},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221113415985478832},
  booktitle={Test},
  volume={8},
  number={1},
  pages={1-73},
  year={1999},
}
N Locantore, JS Marron, DG Simpson, N Tripoli, JT Zhang, KL Cohen, Graciela Boente, Ricardo Fraiman, Babette Brumback, Christophe Croux, Jianqing Fan, Alois Kneip, John I Marden, Daniel Pea, Javier Prieto, Jim O Ramsay, Mariano J Valderrama, and Ana M Aguilera. Robust principal component analysis for functional data. 1999. Vol. 8. In Test. pp.1-73. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221113415985478832.
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