Efficient Online Convolutional Dictionary Learning Using Approximate Sparse Components
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A4 Artikkeli konferenssijulkaisussa
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Date
2023
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en
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5
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Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Abstract
Most available convolutional dictionary learning (CDL) methods use a batch-learning strategy, which consists of alternating optimization of the dictionary and the sparse representations using a training dataset. The computational efficiency of CDL can be improved using an online-learning approach, where the dictionary is optimized incrementally following a sparse approximation of each training sample. However, the existing online CDL (OCDL) methods are still computationally costly when learning large dictionaries. In this paper, we propose an OCDL approach that incorporates decomposed sparse approximations instead of the training samples and substantially improves the computational costs of the existing CDL methods. The resulting optimization problem is addressed using the alternating direction method of multipliers (ADMM).Description
Publisher Copyright: © 2023 IEEE.
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Ghorbani Veshki, F & Vorobyov, S A 2023, Efficient Online Convolutional Dictionary Learning Using Approximate Sparse Components . in Proceedings of the International Conference on Acoustics, Speech, and Signal Processing . ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, IEEE, IEEE International Conference on Acoustics, Speech, and Signal Processing, Rhodes Island, Greece, 04/06/2023 . https://doi.org/10.1109/ICASSP49357.2023.10096444