Adaptive dimensionality reduction for neural network-based online principal component analysis

N. Migenda, R. Möller, W. Schenck, PLOS ONE 16 (2021).

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Artikel | Veröffentlicht | Englisch
Autor*in
Migenda, Nico; Möller, Ralf; Schenck, WolframFH Bielefeld
Abstract
“Principal Component Analysis” (PCA) is an established linear technique for dimensionality reduction. It performs an orthonormal transformation to replace possibly correlated variables with a smaller set of linearly independent variables, the so-called principal components, which capture a large portion of the data variance. The problem of finding the optimal number of principal components has been widely studied for offline PCA. However, when working with streaming data, the optimal number changes continuously. This requires to update both the principal components and the dimensionality in every timestep. While the continuous update of the principal components is widely studied, the available algorithms for dimensionality adjustment are limited to an increment of one in neural network-based and incremental PCA. Therefore, existing approaches cannot account for abrupt changes in the presented data. The contribution of this work is to enable in neural network-based PCA the continuous dimensionality adjustment by an arbitrary number without the necessity to learn all principal components. A novel algorithm is presented that utilizes several PCA characteristics to adaptivly update the optimal number of principal components for neural network-based PCA. A precise estimation of the required dimensionality reduces the computational effort while ensuring that the desired amount of variance is kept. The computational complexity of the proposed algorithm is investigated and it is benchmarked in an experimental study against other neural network-based and incremental PCA approaches where it produces highly competitive results.
Erscheinungsjahr
Zeitschriftentitel
PLOS ONE
Band
16
Zeitschriftennummer
3
Artikelnummer
e0248896
eISSN
FH-PUB-ID

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Migenda, Nico ; Möller, Ralf ; Schenck, Wolfram: Adaptive dimensionality reduction for neural network-based online principal component analysis. In: PLOS ONE Bd. 16, Public Library of Science (PLoS) (2021), Nr. 3
Migenda N, Möller R, Schenck W. Adaptive dimensionality reduction for neural network-based online principal component analysis. PLOS ONE. 2021;16(3). doi:10.1371/journal.pone.0248896
Migenda, N., Möller, R., & Schenck, W. (2021). Adaptive dimensionality reduction for neural network-based online principal component analysis. PLOS ONE, 16(3). https://doi.org/10.1371/journal.pone.0248896
@article{Migenda_Möller_Schenck_2021, title={Adaptive dimensionality reduction for neural network-based online principal component analysis}, volume={16}, DOI={10.1371/journal.pone.0248896}, number={3e0248896}, journal={PLOS ONE}, publisher={Public Library of Science (PLoS)}, author={Migenda, Nico and Möller, Ralf and Schenck, Wolfram}, year={2021} }
Migenda, Nico, Ralf Möller, and Wolfram Schenck. “Adaptive Dimensionality Reduction for Neural Network-Based Online Principal Component Analysis.” PLOS ONE 16, no. 3 (2021). https://doi.org/10.1371/journal.pone.0248896.
N. Migenda, R. Möller, and W. Schenck, “Adaptive dimensionality reduction for neural network-based online principal component analysis,” PLOS ONE, vol. 16, no. 3, 2021.
Migenda, Nico, et al. “Adaptive Dimensionality Reduction for Neural Network-Based Online Principal Component Analysis.” PLOS ONE, vol. 16, no. 3, e0248896, Public Library of Science (PLoS), 2021, doi:10.1371/journal.pone.0248896.

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