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Large-scale independent component analysis by speeding up Lie group techniques

  • We are interested in computing a mini-batch-capable end-to-end algorithm to identify statistically independent components (ICA) in large scale and high-dimensional datasets. Current algorithms typically rely on pre-whitened data and do not integrate the two procedures of whitening and ICA estimation. Our online approach estimates a whitening and a rotation matrix with stochastic gradient descent on centered or uncentered data. We show that this can be done efficiently by combining Batch Karhunen-Löwe-Transformation [1] with Lie group techniques. Our algorithm is recursion-free and can be organized as feed-forward neural network which makes the use of GPU acceleration straight-forward. Because of the very fast convergence of Batch KLT, the gradient descent in the Lie group of orthogonal matrices stabilizes quickly. The optimization is further enhanced by integrating ADAM [2], an improved stochastic gradient descent (SGD) technique from the field of deep learning. We test the scaling capabilities by computing the independent components of the well-known ImageNet challenge (144 GB). Due to its robustness with respect to batch and step size, our approach can be used as a drop-in replacement for standard ICA algorithms where memory is a limiting factor.

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Metadaten
Author:Matthias Hermann, Georg UmlaufORCiDGND, Matthias O. FranzORCiDGND
DOI:https://doi.org/10.1109/ICASSP43922.2022.9746444
ISBN:978-1-6654-0540-9
ISBN:978-1-6654-0541-6
Parent Title (English):International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022, 22 - 27 May, Singapore
Publisher:IEEE
Document Type:Conference Proceeding
Language:English
Year of Publication:2022
Release Date:2022/12/07
Tag:ICA; Lie group; ADAM
Page Number:5 Seiten
First Page:4388
Last Page:4392
Note:
Volltext im Campusnetz via Datenbank IEEE Xplore abrufbar.
Relevance:Peer reviewed nach anderen Listungen (mit Nachweis zum Peer Review Verfahren)
Open Access?:Nein
Licence (German):License LogoUrheberrechtlich geschützt