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Pregled bibliografske jedinice broj: 892794

Multi-view low-rank sparse subspace clustering


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 (međunarodna recenzija, članak, znanstveni)


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Naslov
Multi-view low-rank sparse subspace clustering

Autori
Brbić, Maria ; Kopriva, Ivica

Izvornik
Pattern recognition (0031-3203) 73 (2018); 247-258

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

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

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Matematika, Računarstvo



POVEZANOST RADA


Projekti:
HRZZ-IP-2016-06-5235 - Strukturne dekompozicije empirijskih podataka za računalno potpomognutu dijagnostiku bolesti (DEDAD) (Kopriva, Ivica, HRZZ - 2016-06) ( POIROT)

Ustanove:
Institut "Ruđer Bošković", Zagreb

Profili:

Avatar Url Ivica Kopriva (autor)

Avatar Url Maria Brbić (autor)

Citiraj ovu publikaciju:

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 (međunarodna recenzija, članak, znanstveni)
Brbić, M. & Kopriva, I. (2018) Multi-view low-rank sparse subspace clustering. Pattern recognition, 73, 247-258 doi:10.1016/j.patcog.2017.08.024.
@article{article, year = {2018}, pages = {247-258}, DOI = {10.1016/j.patcog.2017.08.024}, keywords = {Subspace clustering, Multi-view data, Low-rank, Sparsity, Alternating direction method of multipliers, Reproducing kernel Hilbert space}, journal = {Pattern recognition}, doi = {10.1016/j.patcog.2017.08.024}, volume = {73}, issn = {0031-3203}, title = {Multi-view low-rank sparse subspace clustering}, keyword = {Subspace clustering, Multi-view data, Low-rank, Sparsity, Alternating direction method of multipliers, Reproducing kernel Hilbert space} }
@article{article, year = {2018}, pages = {247-258}, DOI = {10.1016/j.patcog.2017.08.024}, keywords = {Subspace clustering, Multi-view data, Low-rank, Sparsity, Alternating direction method of multipliers, Reproducing kernel Hilbert space}, journal = {Pattern recognition}, doi = {10.1016/j.patcog.2017.08.024}, volume = {73}, issn = {0031-3203}, title = {Multi-view low-rank sparse subspace clustering}, keyword = {Subspace clustering, Multi-view data, Low-rank, Sparsity, Alternating direction method of multipliers, Reproducing kernel Hilbert space} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


Citati:





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