Transformation invariant subspace clustering. (November 2016)

• Record Type: Journal Article
• Authors: Li, Qi; Sun, Zhenan; Lin, Zhouchen; He, Ran; Tan, Tieniu
• Is Part Of: Pattern recognition. Volume 59(2016:Nov.)
• Page Start: 142
• Page End: 155
• Publication Date: 2016-11
• Subjects: Transformation -- Subspace clustering -- Joint alignment and clustering; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2016.02.006
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: Subspace clustering has achieved great success in many computer vision applications. However, most subspace clustering algorithms require well aligned data samples, which is often not straightforward to achieve. This paper proposes a Transformation Invariant Subspace Clustering framework by jointly aligning data samples and learning subspace representation. By alignment, the transformed data samples become highly correlated and a better affinity matrix can be obtained. The joint problem can be reduced to a sequence of Least Squares Regression problems, which can be efficiently solved. We verify the effectiveness of the proposed method with extensive experiments on unaligned real data, demonstrating its higher clustering accuracy than the state-of-the-art subspace clustering and transformation invariant clustering algorithms. Abstract : Highlights: A transformation invariant subspace clustering algorithm is proposed. Alignment and subspace clustering are mutually dependent. State-of-the-art clustering results on the unconstrained face dataset.