论文标题
DimenFix：一种新颖的元二维减少方法，用于特征保存
DimenFix: A novel meta-dimensionality reduction method for feature preservation
论文作者
论文摘要
降低的降低已成为一个重要的研究主题，因为近年来解释高维数据集的需求一直在迅速增加。在将数据点映射到较低维空间时，有许多维度降低方法具有良好的性能。但是，这些现有方法无法纳入功能之间的重要性差异。 为了解决这个问题，我们提出了一种新型的元方法，DimenFix可以在任何涉及梯度变季过程的基础维度降低方法上进行操作。通过允许用户定义不同功能的重要性，在降低维度中，DimenFix创造了新的可能性来可视化和理解给定的数据集。同时，DimenFix不会增加时间成本或降低相对于所使用的基本维度降低的尺寸降低质量。
Dimensionality reduction has become an important research topic as demand for interpreting high-dimensional datasets has been increasing rapidly in recent years. There have been many dimensionality reduction methods with good performance in preserving the overall relationship among data points when mapping them to a lower-dimensional space. However, these existing methods fail to incorporate the difference in importance among features. To address this problem, we propose a novel meta-method, DimenFix, which can be operated upon any base dimensionality reduction method that involves a gradient-descent-like process. By allowing users to define the importance of different features, which is considered in dimensionality reduction, DimenFix creates new possibilities to visualize and understand a given dataset. Meanwhile, DimenFix does not increase the time cost or reduce the quality of dimensionality reduction with respect to the base dimensionality reduction used.
