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Title: Multiscale principal component analysis
Authors: Akinduko, A. A.
Gorban, Alexander N.
First Published: 2014
Presented at: 2nd International Conference on Mathematical Modeling in Physical Sciences 2013 (IC-MSQUARE 2013),
Publisher: IOP Publishing
Citation: Journal of Physics: Conference Series 2014, 490 012081
Abstract: Principal component analysis (PCA) is an important tool in exploring data. The conventional approach to PCA leads to a solution which favours the structures with large variances. This is sensitive to outliers and could obfuscate interesting underlying structures. One of the equivalent definitions of PCA is that it seeks the subspaces that maximize the sum of squared pairwise distances between data projections. This definition opens up more flexibility in the analysis of principal components which is useful in enhancing PCA. In this paper we introduce scales into PCA by maximizing only the sum of pairwise distances between projections for pairs of datapoints with distances within a chosen interval of values [l,u]. The resulting principal component decompositions in Multiscale PCA depend on point (l,u) on the plane and for each point we define projectors onto principal components. Cluster analysis of these projectors reveals the structures in the data at various scales. Each structure is described by the eigenvectors at the medoid point of the cluster which represent the structure. We also use the distortion of projections as a criterion for choosing an appropriate scale especially for data with outliers. This method was tested on both artificial distribution of data and real data. For data with multiscale structures, the method was able to reveal the different structures of the data and also to reduce the effect of outliers in the principal component analysis.
DOI Link: 10.1088/1742-6596/490/1/012081
ISSN: 1742-6588
eISSN: 1742-6596
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence (CC BY 3.0) ( Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
Appears in Collections:Published Articles, Dept. of Mathematics

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