Dimension reduction (DR) is commonly utilized to capture the intrinsic
structure and transform high-dimensional data into low-dimensional space while
retaining meaningful properties of the original data. It is used in various
applications, such as image recognition, single-cell sequencing analysis, and
biomarker discovery. However, contemporary parametric-free and parametric DR
techniques suffer from several significant shortcomings, such as the inability
to preserve global and local features and the pool generalization performance.
On the other hand, regarding explainability, it is crucial to comprehend the
embedding process, especially the contribution of each part to the embedding
process, while understanding how each feature affects the embedding results
that identify critical components and help diagnose the embedding process. To
address these problems, we have developed a deep neural network method called
EVNet, which provides not only excellent performance in structural
maintainability but also explainability to the DR therein. EVNet starts with
data augmentation and a manifold-based loss function to improve embedding
performance. The explanation is based on saliency maps and aims to examine the
trained EVNet parameters and contributions of components during the embedding
process. The proposed techniques are integrated with a visual interface to help
the user to adjust EVNet to achieve better DR performance and explainability.
The interactive visual interface makes it easier to illustrate the data
features, compare different DR techniques, and investigate DR. An in-depth
experimental comparison shows that EVNet consistently outperforms the
state-of-the-art methods in both performance measures and explainability.

本论文提出了一种深度神经网络方法（称为 EVNet），用于实现可解释性和结构可保持的降维，该方法基于数据扩增和基于流形的损失函数，在维持准确度和完整性的同时提供解释性，同时整合了视觉界面，以帮助用户调整 EVNet 以实现更好的性能和解释能力。