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Online Principal Component Analysis for Evolving Data Streams

  • Monika Grabowska
  • Wojciech Kotłowski
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 935)

Abstract

We consider an online version of the Principal Component Analysis (PCA), where the goal is to keep track of a subspace of small dimension which captures most of the variance of the data arriving sequentially in a stream. We assume the data stream is evolving and hence the target subspace is changing over time. We cast this problem as a prediction problem, where the goal is to minimize the total compression loss on the data sequence. We review the most popular methods for online PCA and show that the state-of-the-art IPCA algorithm is unable to track the best subspace in this setting. We then propose two modifications of this algorithm, and show that they exhibit a much better predictive performance than the original version of IPCA. Our algorithms are compared against other popular method for online PCA in a computational experiment on real data sets from computer vision.

Keywords

Incremental PCA Online PCA Evolving data streams 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Institute of Computing SciencePoznan University of TechnologyPoznańPoland

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