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Erschienen in:
2018 | OriginalPaper | Buchkapitel
Online Principal Component Analysis for Evolving Data Streams
verfasst von : Monika Grabowska, Wojciech Kotłowski
Erschienen in: Computer and Information Sciences
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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.