Low-Rank Models in Subspace Clustering

Nowadays we are in the big data era,where the data is usually high dimensional.How to process high dimensional data effectively is a critical issue.Fortunately,we observe that data usually distribute near low dimensional manifolds.Mixture of subspaces is a simple yet effective model to represent high dimensional data,where the membership of the data points to the subspaces might be unknown.Therefore,there is a need to simultaneously cluster the data into multiple subspaces and find a low-dimensional subspace fitting each group of data points.This problem,known as subspace clustering,has found numerous applications.In this talk,I will present my work that applies low-rankness to this research problem.Nowadays we are in the big data era,where the data is usually high dimensional.How to process high dimensional data effectively is a critical issue.Fortunately,we observe that data usually distribute near low dimensional manifolds.Mixture of subspaces is a simple yet effective model to represent high dimensional data,where the membership of the data points to the subspaces might be unknown.Therefore,there is a need to simultaneously cluster the data into multiple subspaces and find a low-dimensional subspa中国自动化学会