LAPACK Users' guide (third ed.)
LAPACK Users' guide (third ed.)
Spectral Grouping Using the Nyström Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Using a Global Parameter for Gaussian Affinity Matrices in Spectral Clustering
High Performance Computing for Computational Science - VECPAR 2008
Parallel Spectral Clustering in Distributed Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical framework for plot de-interlacing of TV series based on speakers, dialogues and images
Proceedings of the 2012 ACM international workshop on Audio and multimedia methods for large-scale video analysis
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Spectral Clustering is one of the most important method based on space dimension reduction used in Pattern Recognition. This method consists in selecting dominant eigenvectors of a matrix called affinity matrix in order to define a low-dimensional data space in which data points are easy to cluster. By exploiting properties of Spectral Clustering, we propose a method where we apply independently the algorithm on particular subdomains and gather the results to determine a global partition. Additionally, with a criterion for determining the number of clusters, the domain decomposition strategy for parallel spectral clustering is robust and efficient.