Abstract
The present work discusses robust multivariate methods specifically designed for highdimensions. Their implementation in R is presented and their application is illustratedon examples. The first group are algorithms for outlier detection, already introducedelsewhere and implemented in other packages. The value added of the new package isthat all methods follow the same design pattern and thus can use the same graphicaland diagnostic tools. The next topic covered is sparse principal components including anobject oriented interface to the standard method proposed by Zou, Hastie, and Tibshirani(2006) and the robust one proposed by Croux, Filzmoser, and Fritz (2013). Robust partialleast squares (see Hubert and Vanden Branden 2003) as well as partial least squares fordiscriminant analysis conclude the scope of the new package.
Highlights
High-dimensional data are typical in many contemporary applications in scientific areas like genetics, spectral analysis, data mining, image processing, etc. and introduce new challenges to the traditional analytical methods
The next topic covered is sparse principal component analysis including an object oriented interface to the standard method proposed by Zou et al (2006) and the robust one proposed by Croux et al (2013)
The prediction of group membership and/or describing group separation on the basis of a data set with known group labels is a common task in many applications and linear discriminant analysis (LDA) has often been shown to perform best in such classification problems
Summary
High-dimensional data are typical in many contemporary applications in scientific areas like genetics, spectral analysis, data mining, image processing, etc. and introduce new challenges to the traditional analytical methods. Some of the robust multivariate methods available in R (see Todorov and Filzmoser 2009) are known to deteriorate rapidly when the dimensionality of data increases and others are not applicable at all when p is larger than n. The present work discusses robust multivariate methods designed for high dimensions. Their implementation in R is presented and their application is illustrated on examples. Robust partial least squares (Hubert and Vanden Branden 2003; Sernels, Croux, Filzmoser, and van Espen 2005) as well as partial least squares for discriminant analysis are presented in Section 3 and Section 4.
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