Robust Subspace Clustering via Thresholding Ridge Regression

Given a data set from a union of multiple linear subspaces, a robust subspace clustering algorithm fits each group of data points with a low-dimensional subspace and then clusters these data even though they are grossly corrupted or sampled from the union of dependent subspaces. Under the framework of spectral clustering, recent works using sparse representation, low rank representation and their extensions achieve robust clustering results by formulating the errors (e.g., corruptions) into their objective functions so that the errors can be removed from the inputs. However, these approaches have suffered from the limitation that the structure of the errors should be known as the prior knowledge. In this paper, we present a new method of robust subspace clustering by eliminating the effect of the errors from the projection space (representation) rather than from the input space. We firstly prove that ℓ1-, ℓ2-, and ℓ∞-norm-based linear projection spaces share the property of ...

• Type: Journal
• Year: 2015
• Where: AAAI
• Authors: Xi Peng 0001, Zhang Yi 0001, Huajin Tang
• Keywords: AAAI 2015, Intelligent Agents