In this paper we show that observations in a mixture can be modeled using a union of subspaces, and hence mixture regression can be posed as a subspace clustering problem. This allows to perform mixture regression even in the presence of missing data. We illustrate this using a state-of-the-art subspace clustering algorithm for incomplete data to perform mixed linear regression on gene functional data. Our approach outperforms existing methods on this task.
展开▼
