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

“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 por...

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Bibliographic Details
Published in:PloS one Vol. 16; no. 3; p. e0248896
Main Authors: Migenda, Nico, Möller, Ralf, Schenck, Wolfram
Format: Journal Article
Language:English
Published: United States Public Library of Science 30.03.2021
Public Library of Science (PLoS)
Subjects:
Algorithms
Biology and Life Sciences
Computer and Information Sciences
Computer applications
Data points
Datasets
Distance learning
Eigenvalues
Electronic data processing
Machine learning
Methods
Neural networks
Physical Sciences
Principal components analysis
Research and Analysis Methods
Germany
ISSN:1932-6203, 1932-6203
Online Access:Get full text
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Summary:“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.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0248896