A Multi-level Feature Extraction Method for Heterogeneous Sample Enhancement

The challenge of dealing with small sample sizes is a significant issue in data-driven modeling. To address this challenge, domain adaptation (DA) techniques have been developed to transfer samples from a source domain to a target domain to enhance the sample size. However, in real-world applications, the existing DA methods have limited applicability due to the lack of effective feature extraction approaches. To overcome these limitations, this paper introduces a novel multi- level feature extraction method for enhancing heterogeneous samples. The proposed method decomposes sample features into three orthogonal dimensions to more accurately express sample information. The results demonstrate that our method outperforms representative methods, highlighting the effectiveness of our approach in addressing the challenges of small sample sizes and domain adaptation.