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Multi-view spectral clustering via partial sum minimisation of singular values

Multi-view spectral clustering via partial sum minimisation of singular values

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This Letter proposes a robust multi-view spectral clustering approach. It first calculates a normalised graph Laplacian for each single view, and then uses them to recover a shared low-rank Laplacian by the low rank and sparse matrix decomposition. To achieve matrix decomposition, partial sum minimisation of singular values is leveraged to design a novel objective function, which can be optimised by the augmented Lagrangian multiplier algorithm to recover a common normalised graph Laplacian. Accordingly, multi-view clustering results can be obtained by taking spectral clustering on the common Laplacian. Experimental results illustrate its effectiveness over other related approaches.

References

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      • 2. Zhou, D., Burges, C.J.C.: ‘Spectral clustering and transductive learning with multiple views’. Machine Learning, Proc. of the Twenty-Fourth Int. Conf., Corvallis, OR, June 2007.
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      • 3. Xia, R., Pan, Y., Du, L., et al: ‘Robust multi-view spectral clustering via low-rank and sparse decomposition’. Twenty-Eighth AAAI Conf. on Artificial Intelligence, Québec City, QC, Canada, July 2014.
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      • 5. Cao, X., Zhang, C., Fu, H., et al: ‘Diversity-induced multi-view subspace clustering’. Computer Vision and Pattern Recognition, Boston, MA, USA, June 2015.
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http://iet.metastore.ingenta.com/content/journals/10.1049/el.2018.7883
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