document: Parallel computation of high dimensional robust correlation and covariance matrices The computation of covariance and correlation matrices are critical to many data mining applications and processes . Unfortunately the classical covariance and correlation matrices are very sensitive to outliers . Robust methods , such as QC and the Maronna method , have been proposed . However , existing algorithms for QC only give acceptable performance when the dimensionality of the matrix is in the hundreds ; and the Maronna method is rarely used in practice because of its high computational cost . In this paper , we develop parallel algorithms for both QC and the Maronna method . We evaluate these parallel algorithms using a real data set of the gene expression of over 6,000 genes , giving rise to a matrix of over 18 million entries . In our experimental evaluation , we explore scalability in dimensionality and in the number of processors . We also compare the parallel behaviors of the two methods . After thorough experimentation , we conclude that for many data mining applications , both QC and Maronna are viable options . Less robust , but faster , QC is the recommended choice for small parallel platforms . On the other hand , the Maronna method is the recommended choice when a high degree of robustness is required , or when the parallel platform features a high number of processors .
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