Deep-RLS: A Model-Inspired Deep Learning Approach to Nonlinear PCA

• URL: http://arxiv.org/abs/2011.07458v2
• Date: Wed, 18 Nov 2020 04:45:01 GMT
• Title: Deep-RLS: A Model-Inspired Deep Learning Approach to Nonlinear PCA
• Authors: Zahra Esmaeilbeig, Shahin Khobahi, Mojtaba Soltanalian
• Abstract summary: We propose a task-based deep learning approach, referred to as Deep-RLS, to perform nonlinear PCA. In particular, we formulate the nonlinear PCA for the blind source separation (BSS) problem and show through numerical analysis that Deep-RLS results in a significant improvement in the accuracy of recovering the source signals.
• Score: 12.629088975832797
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we consider the application of model-based deep learning in nonlinear principal component analysis (PCA). Inspired by the deep unfolding methodology, we propose a task-based deep learning approach, referred to as Deep-RLS, that unfolds the iterations of the well-known recursive least squares (RLS) algorithm into the layers of a deep neural network in order to perform nonlinear PCA. In particular, we formulate the nonlinear PCA for the blind source separation (BSS) problem and show through numerical analysis that Deep-RLS results in a significant improvement in the accuracy of recovering the source signals in BSS when compared to the traditional RLS algorithm.

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