通过交替加载多源数据提升模型泛化能力，减少训练时间。
Selective Embedding for Deep Learning
- 将多源数据短段交替输入单通道，模仿人类信息处理方式。
- 在6个时序数据集上保持高分类准确率，训练时间显著缩短。
- 适合医疗、机械等复杂多源场景，兼顾效率与鲁棒性。
深度学习通过自动从原始数据中学习复杂模式，推动多个行业变革，减少了对手工特征工程的依赖。然而，深度学习模型对输入数据敏感，在非平稳条件和不同领域间性能常下降，尤其在时序数据上更为明显。传统单通道或并行多源数据加载策略要么限制泛化能力，要么增加计算开销。本文提出选择性嵌入（Selective Embedding），一种新型数据加载策略，通过在单输入通道中交替插入来自多个数据源的短片段，受认知心理学启发，模拟人类信息处理机制，以降低模型过拟合，增强泛化能力，并提高计算效率。在六个时序数据集上的验证表明，该方法在多种深度学习架构下均能保持高分类准确率，同时显著缩短训练时间。该方法在具有多源数据的复杂系统中表现尤为出色，为医疗、重型机械、海洋、铁路及农业等需要强鲁棒性和适应性的现实应用提供可扩展、资源高效的解决方案。
原文摘要 · Abstract (English)
Deep learning has revolutionized many industries by enabling models to automatically learn complex patterns from raw data, reducing dependence on manual feature engineering. However, deep learning algorithms are sensitive to input data, and performance often deteriorates under nonstationary conditions and across dissimilar domains, especially when using time-domain data. Conventional single-channel or parallel multi-source data loading strategies either limit generalization or increase computational costs. This study introduces selective embedding, a novel data loading strategy, which alternates short segments of data from multiple sources within a single input channel. Drawing inspiration from cognitive psychology, selective embedding mimics human-like information processing to reduce model overfitting, enhance generalization, and improve computational efficiency. Validation is conducted using six time-domain datasets, demonstrating that the proposed method consistently achieves high classification accuracy across various deep learning architectures while significantly reducing training times. The approach proves particularly effective for complex systems with multiple data sources, offering a scalable and resource-efficient solution for real-world applications in healthcare, heavy machinery, marine, railway, and agriculture, where robustness and adaptability are critical.
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