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Adaptive Dimensionality Adjustment for Online “Principal Component Analysis”

  • Nico MigendaEmail author
  • Ralf Möller
  • Wolfram Schenck
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11871)

Abstract

Many applications in the Industrial Internet of Things and Industry 4.0 rely on large amounts of data which are continuously generated. The exponential growth in available data and the resulting storage requirements are often underestimated bottlenecks. Therefore, efficient dimensionality reduction gets more attention and becomes more relevant. One of the most widely used techniques for dimensionality reduction is “Principal Component Analysis” (PCA). A novel algorithm to determine the optimal number of meaningful principal components on a data stream is proposed. The basic idea of the proposed algorithm is to optimize the dimensionality adjustment process by taking advantage of several “natural” PCA features. In contrast to the commonly used approach to start with a maximal set of principal components and apply some sort of stopping rule, the proposed algorithm starts with a minimal set of principal components and uses a linear regression model in the natural logarithmic scale to approximate the remaining components. An experimental study is presented to demonstrate the successful application of the algorithm to noisy synthetic and real world data sets.

Keywords

Principal Component Analysis Adaptive dimensionality adjustment Stopping rule Big data Industry 4.0 Industrial IoT 

Notes

Acknowledgements

This work was supported by the EFRE-NRW funding programme “Forschungsinfrastrukturen” (grant no. 34.EFRE-0300119).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Center for Applied Data Science Gütersloh, Faculty of Engineering and MathematicsBielefeld University of Applied SciencesBielefeldGermany
  2. 2.Computer Engineering Group, Faculty of TechnologyBielefeld UniversityBielefeldGermany

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