On a strategy for Spectral Clustering with parallel computation
Sandrine Mouysset (IRIT-ENSEEIHT)Joseph Noailles (IRIT-ENSEEIHT)
Daniel Ruiz (IRIT-ENSEEIHT)
Ronan Guivarch (IRIT-ENSEEIHT)
Abstract:
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.
Keywords:
Numerical Algorithms for CS&E
