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