Lightweight Adaptive ReduNet via Hyperspherical Manifold Learning

• URL: http://arxiv.org/abs/2608.20668v1
• Date: Fri, 21 Aug 2026 01:58:51 GMT
• Title: Lightweight Adaptive ReduNet via Hyperspherical Manifold Learning
• Abstract summary: We propose LA-ReduNet, a lightweight adaptive architecture that refines the layer-wise update rule.<n>We show that LA-ReduNet requires significantly fewer layers for the MCR$2$ objective to reach a stable value.<n>Remarkably, under the considered experimental settings, LA-ReduNet requires only approximately $1/29$ of the parameter storage of the unfolded ReduNet module.
• Score: 30.408921338315093
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, a white-box neural network called ReduNet has been proposed, which employs the maximal coding rate reduction (MCR$^2$) principle to transform raw data into low-dimensional discriminative features via a forward layer-wise construction process. Unlike traditional deep networks that rely on backpropagation, ReduNet explicitly derives the parameters of each layer from the features of its preceding layer, offering a mathematically interpretable paradigm. However, this layer-wise construction often requires a large number of layers for the MCR$^2$ objective to reach a stable value, which increases the parameter storage of the unfolded module. To address this issue, we propose LA-ReduNet, a lightweight adaptive architecture that refines the layer-wise update rule and enables discriminative feature representations to be obtained with substantially fewer unfolded layers. Specifically, LA-ReduNet employs hyperspherical manifold learning and adaptive step sizes, thereby reducing by an order of magnitude the number of layers required for the MCR$^2$ objective to reach a stable value. Simulation results demonstrate that, while maintaining comparable classification accuracy, LA-ReduNet requires significantly fewer layers for the MCR$^2$ objective to reach a stable value. Remarkably, under the considered experimental settings, LA-ReduNet requires only approximately $1/29$ of the parameter storage of the unfolded ReduNet module for the MCR$^2$ objective to reach a stable value.

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