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Machine Vision and Applications
Erschienen in: Machine Vision and Applications 6/2018

28.02.2018 | Special Issue Paper

Rank–sparsity balanced representation for subspace clustering

verfasst von: Yuqing Xia, Zhenyue Zhang

Erschienen in: Machine Vision and Applications | Ausgabe 6/2018

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Abstract

Subspace learning has many applications such as motion segmentation and image recognition. The existing algorithms based on self-expressiveness of samples for subspace learning may suffer from the unsuitable balance between the rank and sparsity of the expressive matrix. In this paper, a new model is proposed that can balance the rank and sparsity well. This model adopts the log-determinant function to control the rank of solution. Meanwhile, the diagonals are penalized, rather than the strict zero-restriction on diagonals. This strategy makes the rank–sparsity balance more tunable. We furthermore give a new graph construction from the low-rank and sparse solution, which absorbs the advantages of the graph constructions in the sparse subspace clustering and the low-rank representation for further clustering. Numerical experiments show that the new method, named as RSBR, can significantly increase the accuracy of subspace clustering on the real-world data sets that we tested.
Vorheriger Artikel Biological modeling of human visual system for object recognition using GLoP filters and sparse coding on multi-manifolds
Nächster Artikel Extended sparse representation-based classification method for face recognition

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Fußnoten
1
We say \(\mathbf{C}_k\) is connected if a undirected graph having the adjoint matrix \(|\mathbf{C}_k|+|\mathbf{C}_k^T|\) is connected.
 
6
In this case, \(\mathbf{C}\) should be a block-diagonal matrix of K connected blocks under a permutation.
 
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Metadaten
Titel
Rank–sparsity balanced representation for subspace clustering
verfasst von
Yuqing Xia
Zhenyue Zhang
Publikationsdatum
28.02.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
Machine Vision and Applications / Ausgabe 6/2018
Print ISSN: 0932-8092
Elektronische ISSN: 1432-1769
DOI
https://doi.org/10.1007/s00138-018-0918-y

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