.png){kind=icon}
返回
Symmetry-Aware Feature Representations and Model Optimization for Interpretable Machine Learning
.png){kind=icon}
OA
AI
DOI：10.3390/sym17111821.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper investigates the role of symmetry and asymmetry in the learning process of modern machine learning models, with a specific focus on feature representation and optimization. We introduce a novel symmetry-aware learning framework that identifies and preserves symmetric properties within high-dimensional datasets, while allowing model asymmetries to capture essential discriminative cues. Through analytical modeling and empirical evaluations on benchmark datasets, we demonstrate how symmetrical transformations of features (e.g., rotation, mirroring, permutation invariance) impact learning efficiency, interpretability, and generalization. Furthermore, we explore asymmetric regularization techniques that prioritize informative deviations from symmetry in model parameters, thereby improving classification and clustering performance. The proposed approach is validated using a variety of classifiers including neural networks and tested across domains such as image recognition, biomedical data, and social networks. Our findings highlight the critical importance of leveraging domain-specific symmetries to enhance both the performance and explainability of machine learning systems.
Keyword:
interpretability
feature representation
image classification
symmetry in machine learning
asymmetry regularization
equivariance
group theory
geometric deep learning
RotMNIST
ECG classification
graph neural networks
SHAP
Grad-CAM
model robustness
structured representations
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
S
IF:
2.2
论文数:
1.4K
被引数:
0
机构
引用论文
"Why Should I Trust You?"“我为什么要相信你？”.png)
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data MiningIF0
PREAI
分享收藏A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI可解释人工智能 (XAI) 调查: 走向医学XAIOAAI
OAAI分享收藏Gradient-based learning applied to document recognition基于梯度的学习在文档识别中的应用PROCEEDINGS OF THE IEEE
PROCEEDINGS OF THE IEEEIF25.9
PREAI
分享收藏A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification基于深度双向LSTM网络模型的小波序列心电信号分类
PREAI
分享收藏
查看更多内容