用最小描述长度优化VAE，提升妇科数据重建精度与稳定性
Comparative Analysis of MDL-VAE vs. Standard VAE on 202 Years of Gynecological Data
- 引入MDL正则化改进VAE，增强潜在空间结构
- 重建误差显著降低，均方误差等指标更优
- 训练稳定、推理高效，适合医疗数据建模
本研究对比了基于最小描述长度（MDL）正则化的变分自编码器（MDL-VAE）与标准自编码器在202年妇科高维数据上的重构表现。MDL-VAE展现出更低的重构误差（MSE、MAE、RMSE），并生成更具结构化的潜在表示，归因于有效的KL散度正则化。统计分析证实性能提升具有显著性。此外，MDL-VAE表现出一致的训练与验证损失，且推理时间高效，体现出良好的鲁棒性与实际应用潜力。研究结果表明，在VAE架构中融入MDL原理可显著提升数据重构与泛化能力，为医疗数据建模与分析提供有前景的新方法。
原文摘要 · Abstract (English)
This study presents a comparative evaluation of a Variational Autoencoder (VAE) enhanced with Minimum Description Length (MDL) regularization against a Standard Autoencoder for reconstructing high-dimensional gynecological data. The MDL-VAE exhibits significantly lower reconstruction errors (MSE, MAE, RMSE) and more structured latent representations, driven by effective KL divergence regularization. Statistical analyses confirm these performance improvements are significant. Furthermore, the MDL-VAE shows consistent training and validation losses and achieves efficient inference times, underscoring its robustness and practical viability. Our findings suggest that incorporating MDL principles into VAE architectures can substantially improve data reconstruction and generalization, making it a promising approach for advanced applications in healthcare data modeling and analysis.
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