Abstract
Spectral clustering is one of the most relevant unsupervised method able to gather data without a priori information on shapes or locality. A parallel strategy based on domain decomposition with overlapping interface is reminded. By investigating sparsification techniques and introducing sparse structures, this parallel method is adapted to treat very large data set in fields of Pattern Recognition and Image Segmentation.
This work was performed using HPC resources from CALMIP (Grant 2012-p0989).
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Mouysset, S., Guivarch, R. (2013). Sparsification on Parallel Spectral Clustering. In: Daydé, M., Marques, O., Nakajima, K. (eds) High Performance Computing for Computational Science - VECPAR 2012. VECPAR 2012. Lecture Notes in Computer Science, vol 7851. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38718-0_25
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DOI: https://doi.org/10.1007/978-3-642-38718-0_25
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