  Efficiently computing a subset of a correlation matrix consisting of values
above a specified threshold is important to many practical applications.
Real-world problems in genomics, machine learning, finance other applications
can produce correlation matrices too large to explicitly form and tractably
compute. Often, only values corresponding to highly-correlated vectors are of
interest, and those values typically make up a small fraction of the overall
correlation matrix. We present a method based on the singular value
decomposition (SVD) and its relationship to the data covariance structure that
can efficiently compute thresholded subsets of very large correlation matrices.
