Dimensionality reduction is widely used in the visualization, compression, exploration and classification of data. Yet a generally applicable solution remains unavailable. Here, we report an accurate and broadly applicable data-driven algorithm for dimensionality reduction. The algorithm, which we named ‘feature-augmented embedding machine’ (FEM), first learns the structure of the data and the inherent characteristics of the data components (such as central tendency and dispersion), denoises the data, increases the separation of the components, and then projects the data onto a lower number of dimensions. We show that the technique is effective at revealing the underlying dominant trends in datasets of protein expression and single-cell RNA sequencing, computed tomography, electroencephalography and wearable physiological sensors.

降维广泛应用于数据的可视化、压缩、探索和分类。然而，普遍适用的解决方案仍然不可用。在这里，我们报告了一种准确且广泛适用的数据驱动降维算法。我们命名为“特征增强嵌入机”（FEM）的算法，首先学习数据的结构和数据成分的固有特征（如集中趋势和分散），对数据进行去噪，增加数据的分离度。组件，然后将数据投影到较少的维度上。我们表明，该技术可有效揭示蛋白质表达和单细胞 RNA 测序、计算机断层扫描、脑电图和可穿戴生理传感器数据集中的潜在主导趋势。