.png){kind=icon}
.png){kind=icon}
Return
A Momentum Accelerated Algorithm for ReLU-Based Nonlinear Matrix Decomposition
.png){kind=icon}
PRE
AI
DOI：10.1109/LSP.2024.3475910.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Recently, there has been a growing interest in the exploration of Nonlinear Matrix Decomposition (NMD) due to its close ties with neural networks. NMD aims to find a low-rank matrix from a sparse nonnegativematrixwith a per-element nonlinear function. A typical choice is the Rectified Linear Unit (ReLU) activation function. To address over-fitting in the existing ReLU-based NMD model (ReLU-NMD), we propose a Tikhonov regularized ReLU-NMD model, referred to as ReLU-NMD-T. Subsequently, we introduce a momentum accelerated algorithm for handling the ReLU-NMD-Tmodel. A distinctive feature, setting our work apart from most existing studies, is the incorporation of both positive and negative momentum parameters in our algorithm. Our numerical experiments on real-world datasets show the effectiveness of the proposed model and algorithm.
Keywords:
Alternating minimization
low-rank matrix decomposition
momentum
nonlinearity.
Journal
IF:
9.6
Papers:
1.1W
Citations:
1.7W