Multi-manifold modeling is increasingly used in segmentation and data
representation tasks in computer vision and related fields. While the general
problem, modeling data by mixtures of manifolds, is very challenging, several
approaches exist for modeling data by mixtures of affine subspaces (which is
often referred to as hybrid linear modeling). We translate some important
instances of multi-manifold modeling to hybrid linear modeling in embedded
spaces, without explicitly performing the embedding but applying the kernel
trick. The resulting algorithm, Kernel Spectral Curvature Clustering, uses
kernels at two levels - both as an implicit embedding method to linearize
nonflat manifolds and as a principled method to convert a multiway affinity
problem into a spectral clustering one. We demonstrate the effectiveness of the
method by comparing it with other state-of-the-art methods on both synthetic
data and a real-world problem of segmenting multiple motions from two
perspective camera views.

利用核函数的奇技淫巧在嵌入空间下将多流形建模问题转化为混合线性建模问题，并提出了一种名为 Kernel Spectral Curvature Clustering 的算法，能够通过光谱聚类方法从多方面的相似性中将多种动作区分开来。