Projection pursuit via kernel mean embeddings
针对高维数据线性投影中常见的Diaconis–Freedman效应，提出一种结合全局搜索与局部投影寻踪的方法，通过最大化投影数据经验分布与数据驱动高斯混合分布之间的最大均值差异（MMD）来检测和可视化有趣结构。
Detecting and visualizing interesting structures in high-dimensional data is a ubiquitous challenge. If one aims for linear projections onto low-dimensional spaces, a well-known problematic phenomenon is the Diaconis–Freedman effect: under mild conditions, most projections do not reveal interesting structures but look like scale mixtures of spherically symmetric Gaussian distributions. We present a method which combines global search strategies and local projection pursuit via maximizing the maximum mean discrepancy (MMD) between the empirical distribution of the projected data and a data-driven Gaussian mixture distribution. Here, MMD is based on kernel mean embeddings with Gaussian kernels.