Algorithms for Projection - Pursuit robust principal component analysis

15/06/2007

The results of a standard principal component analysis (PCA) can be affected by the presence of outliers. Hence robust alternatives to {PCA} are needed. One of the most appealing robust methods for principal component analysis uses the ProjectionxPursuit principle. Here, one projects the data on a lower-dimensional space such that a robust measure of variance of the projected data will be maximized. The ProjectionxPursuit-based method for principal component analysis has recently been introduced in the field of chemometrics, where the number of variables is typically large. In this paper, it is shown that the currently available algorithm for robust ProjectionxPursuit {PCA} performs poor in the presence of many variables. A new algorithm is proposed that is more suitable for the analysis of chemical data. Its performance is studied by means of simulation experiments and illustrated on some real data sets.

Autores da comunidade :

• 

  M

  Maria do Rosário De Oliveira Silva

  ist12954

  Afiliações e fontes

  IST > DM

  Departamento de Matemática

  De: orcid

  ID: 13987670

  De: cienciavitae

  ID: 2883448

  De: scopus

  ID: 2-s2.0-34248577801

AO - Versão original do autor

Elsevier

https://www.sciencedirect.com/science/article/abs/pii/S016974390700007X

Chemometrics and Intelligent Laboratory Systems

218

225

87

2

10.1016/j.chemolab.2007.01.004

mathematics - Matemática

eng - Inglês

https://doi.org/10.1016/j.chemolab.2007.01.004