Learning Subspaces of Different Dimensions
提出一个贝叶斯模型来推断不同维度的子空间混合，通过将子空间嵌入低维球面来设定先验，并给出高效采样算法，可用于主题建模等应用。
We introduce a Bayesian model for inferring mixtures of subspaces of different dimensions. The model allows flexible and efficient learning of a density supported in an ambient space which in fact can concentrate around some lower-dimensional space. The key challenge in such a mixture model is specification of prior distributions over subspaces of different dimensions. We address this challenge by embedding subspaces or Grassmann manifolds into a sphere of relatively low dimension and specifying priors on the sphere. We provide an efficient sampling algorithm for the posterior distribution of the model parameters. We illustrate that a simple extension of our mixture of subspaces model can be applied to topic modeling. The utility of our approach is demonstrated with applications to real and simulated data.