# DAPLSR: Data Augmentation Partial Least Squares Regression Model via Manifold Optimization

arXiv: https://arxiv.org/abs/2504.16639 · HTML: https://mlsanity.com/paper/2504.16639/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 32 references that are in the map.
- First posted on arXiv 23 Apr 2025.

## Builds on (most cited first, 1 of 1)

- [Combination of PCA with SMOTE Resampling to Boost the Prediction Rate in Lung Cancer Dataset](https://mlsanity.com/paper/1403.1949/) (2014)

## Abstract (arXiv)

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.