Robust principal components: A generalized variance perspective

Wilcox, Rand R.

doi:10.3758/brm.40.1.102

Cited by 10 publications

(1 citation statement)

References 27 publications

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“…Table 2 shows the computation time of the algorithms in seconds, averaged over M = 50 replications. Not surprisingly, we see that spherical PCA is the fastest to compute because it does not require any iterative process (see also Maronna, 2005;Wilcox, 2008). For n = 1000 and q = 2 projection-pursuit is relatively fast to compute regardless of the dimension of the data, as can be seen from Table 2.…”

Section: Computation Timementioning

confidence: 68%

Fast computation of robust subspace estimators

Cevallos-Valdiviezo

Aelst

2019

Computational Statistics & Data Analysis

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Dimension reduction is often an important step in the analysis of high-dimensional data. PCA is a popular technique to find the best low-dimensional approximation of high-dimensional data. However, classical PCA is very sensitive to atypical data. Robust methods to estimate the low-dimensional subspace that best approximates the regular data have been proposed. However, for highdimensional data these algorithms become computationally expensive. Alternative algorithms for the robust subspace estimators are proposed that are better suited to compute the solution for high-dimensional problems. The main ingredients of the new algorithms are twofold. First, the principal directions of the subspace are estimated directly by iterating the first order solutions corresponding to the estimators. Second, to reduce the computation time even further five robust deterministic values are proposed to initialize the algorithms instead of using random starting values. It is shown that the new algorithms yield robust solutions and the computation time is largely reduced, especially for high-dimensional data.

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Section: Computation Timementioning

confidence: 68%