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Multi-view low-rank sparse subspace clustering (CROSBI ID 242391)

Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija

Brbić, Maria ; Kopriva, Ivica Multi-view low-rank sparse subspace clustering // Pattern recognition, 73 (2018), 247-258. doi: 10.1016/j.patcog.2017.08.024

Podaci o odgovornosti

Brbić, Maria ; Kopriva, Ivica

engleski

Multi-view low-rank sparse subspace clustering

Most existing approaches address multi-view subspace clustering problem by constructing the affinity matrix on each view separately and afterwards propose how to extend spectral clustering algorithm to handle multi-view data. This paper presents an approach to multi-view subspace clustering that learns a joint subspace representation by constructing affinity matrix shared among all views. Relying on the importance of both low-rank and sparsity constraints in the construction of the affinity matrix, we introduce the objective that balances between the agreement across different views, while at the same time encourages sparsity and low-rankness of the solution. Related low-rank and sparsity constrained optimization problem is for each view solved using the alternating direction method of multipliers. Furthermore, we extend our approach to cluster data drawn from nonlinear subspaces by solving the corresponding problem in a reproducing kernel Hilbert space. The proposed algorithm outperforms state-of-the-art multi- view subspace clustering algorithms on one synthetic and four real-world datasets.

Subspace clustering ; Multi-view data ; Low-rank ; Sparsity ; Alternating direction method of multipliers ; Reproducing kernel Hilbert space

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Podaci o izdanju

73

2018.

247-258

objavljeno

0031-3203

1873-5142

10.1016/j.patcog.2017.08.024

Povezanost rada

Matematika, Računarstvo

Poveznice
Indeksiranost