论文标题
Deep-RLS：一种受模型启发的非线性PCA的深度学习方法
Deep-RLS: A Model-Inspired Deep Learning Approach to Nonlinear PCA
论文作者
论文摘要
在这项工作中，我们考虑基于模型的深度学习在非线性主成分分析（PCA）中的应用。受到深度展开方法的启发，我们提出了一种基于任务的深度学习方法，称为Deep-RLS，它展开了众所周知的递归最小二乘（RLS）算法的迭代，以便为了执行非线性PCA，以进行深神经网络的层次。特别是，我们为盲源分离（BSS）问题制定了非线性PCA，并通过数值分析表明，与传统的RLS算法相比，DeepRLS会导致恢复BSS中源信号的准确性显着提高。
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.
