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Advances in Data Analysis and Classification

Erschienen in:

01.06.2014 | Regular Article

Estimating common principal components in high dimensions

verfasst von: Ryan P. Browne, Paul D. McNicholas

Erschienen in: Advances in Data Analysis and Classification | Ausgabe 2/2014

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Abstract

We consider the problem of minimizing an objective function that depends on an orthonormal matrix. This situation is encountered, for example, when looking for common principal components. The Flury method is a popular approach but is not effective for higher dimensional problems. We obtain several simple majorization–minimization (MM) algorithms that provide solutions to this problem and are effective in higher dimensions. We use mixture model-based clustering applications to illustrate our MM algorithms. We then use simulated data to compare them with other approaches, with comparisons drawn with respect to convergence and computational time.

Vorheriger Artikel Threshold optimization for classification in imbalanced data in a problem of gamma-ray astronomy

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Titel: Estimating common principal components in high dimensions
verfasst von: Ryan P. Browne
Paul D. McNicholas
Publikationsdatum: 01.06.2014
Verlag: Springer Berlin Heidelberg
Erschienen in: Advances in Data Analysis and Classification / Ausgabe 2/2014
Print ISSN: 1862-5347
Elektronische ISSN: 1862-5355
DOI: https://doi.org/10.1007/s11634-013-0139-1

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