Deep-RLS: A Model-Inspired Deep Learning Approach to Nonlinear PCA
Signal Processing
2020-11-19 v2 Machine Learning
Machine Learning
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.
Cite
@article{arxiv.2011.07458,
title = {Deep-RLS: A Model-Inspired Deep Learning Approach to Nonlinear PCA},
author = {Zahra Esmaeilbeig and Shahin Khobahi and Mojtaba Soltanalian},
journal= {arXiv preprint arXiv:2011.07458},
year = {2020}
}