论文标题
具有两相优化的均匀歧管近似
Uniform Manifold Approximation with Two-phase Optimization
论文作者
论文摘要
我们引入了具有两相优化（UMATO）的统一歧管近似，这是一种降低维度（DR）技术，可改善UMAP以更准确地捕获高维数据的全局结构。在Umato中，优化分为两个阶段，因此所得的嵌入可以可靠地描绘出全球结构，同时以足够的精度保存局部结构。在第一阶段，识别和预计将为全局结构构建骨骼布局。在第二阶段，剩余点添加到保留当地区域特征的嵌入。通过定量实验，我们发现Umato（1）在保留全局结构方面优于广泛使用的DR技术，而（2）在代表局部结构方面产生了竞争精度。我们还验证了Umato在鲁棒性方面比各种初始化方法，时期数量和亚采样技术优选。
We introduce Uniform Manifold Approximation with Two-phase Optimization (UMATO), a dimensionality reduction (DR) technique that improves UMAP to capture the global structure of high-dimensional data more accurately. In UMATO, optimization is divided into two phases so that the resulting embeddings can depict the global structure reliably while preserving the local structure with sufficient accuracy. In the first phase, hub points are identified and projected to construct a skeletal layout for the global structure. In the second phase, the remaining points are added to the embedding preserving the regional characteristics of local areas. Through quantitative experiments, we found that UMATO (1) outperformed widely used DR techniques in preserving the global structure while (2) producing competitive accuracy in representing the local structure. We also verified that UMATO is preferable in terms of robustness over diverse initialization methods, number of epochs, and subsampling techniques.
