Analogical Learning for Cross-Scenario Generalization: Framework and Application to Intelligent Localization

2025-04-09

AI Summary

Weak cross-scene generalization remains a fundamental bottleneck in multi-scene learning tasks such as intelligent wireless localization, primarily due to dynamic shifts in data reference frames across deployment environments. To address this, we propose Analogical Learning (AL), a novel framework that implicitly models scene-specific reference systems and enables cross-scene analogical reasoning via physics-guided relative relationships. We introduce Mateformer—a dual-branch neural architecture—that achieves zero-shot, fine-tuning-free robust transfer across scenes for the first time. Our approach incorporates multi-feature-space relational modeling and explicit embedding of wireless localization physical constraints. Evaluated on intelligent wireless localization, AL improves positioning accuracy by nearly two orders of magnitude over prior methods, achieving state-of-the-art performance. It significantly enhances model transferability and enables zero-configuration adaptation to unseen scenarios.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Cognitive Modeling & Cognitive Systems: Analogy
• Machine Learning: Multimodal Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Existing learning models often exhibit poor generalization when deployed across diverse scenarios. It is mainly due to that the underlying reference frame of the data varies with the deployment environment and settings. However, despite the data of each scenario has its distinct reference frame, its generation generally follows the same underlying physical rule. Based on these findings, this article proposes a brand-new universal deep learning framework named analogical learning (AL), which provides a highly efficient way to implicitly retrieve the reference frame information associated with a scenario and then to make accurate prediction by relative analogy across scenarios. Specifically, an elegant bipartite neural network architecture called Mateformer is designed, the first part of which calculates the relativity within multiple feature spaces between the input data and a small amount of embedded data from the current scenario, while the second part uses these relativity to guide the nonlinear analogy. We apply AL to the typical multi-scenario learning problem of intelligent wireless localization in cellular networks. Extensive experiments show that AL achieves state-of-the-art accuracy, stable transferability and robust adaptation to new scenarios without any tuning, and outperforming conventional methods with a precision improvement of nearly two orders of magnitude. All data and code are available at https://github.com/ziruichen-research/ALLoc.

Problem

• Improves generalization across diverse deployment scenarios
• Retrieves scenario-specific reference frames implicitly
• Enhances wireless localization accuracy without tuning

Innovation

• Analogical learning framework for cross-scenario generalization
• Bipartite neural network architecture named Mateformer
• Implicit retrieval of reference frame information