针对类别不均衡数据，提出基于流形优化的增强型偏最小二乘回归模型。
DAPLSR: Data Augmentation Partial Least Squares Regression Model via Manifold Optimization
- 融合SMOTE与VDM生成更贴近原数据的合成样本
- 在多个数据集上分类性能显著优于现有方法
- 适合处理类别不平衡的回归与分类任务
传统偏最小二乘回归（PLSR）在处理类别不均衡数据时表现不佳。为此，本文提出一种基于流形优化的数据增强偏最小二乘回归（DAPLSR）模型。该模型引入合成少数类过采样技术（SMOTE）扩充样本数量，并采用值差异度量（VDM）选择与原始样本高度相似的近邻样本以生成合成样本。为获得更精确的PLSR数值解，本文提出一种利用约束空间几何特性的流形优化方法，有效缓解模型退化与优化问题。大量实验表明，所提DAPLSR模型在多个数据集上均实现优越的分类性能和优异的评估指标，显著优于现有方法。
原文摘要 · Abstract (English)
Traditional Partial Least Squares Regression (PLSR) models frequently underperform when handling data characterized by uneven categories. To address the issue, this paper proposes a Data Augmentation Partial Least Squares Regression (DAPLSR) model via manifold optimization. The DAPLSR model introduces the Synthetic Minority Over-sampling Technique (SMOTE) to increase the number of samples and utilizes the Value Difference Metric (VDM) to select the nearest neighbor samples that closely resemble the original samples for generating synthetic samples. In solving the model, in order to obtain a more accurate numerical solution for PLSR, this paper proposes a manifold optimization method that uses the geometric properties of the constraint space to improve model degradation and optimization. Comprehensive experiments show that the proposed DAPLSR model achieves superior classification performance and outstanding evaluation metrics on various datasets, significantly outperforming existing methods.
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