TY - GEN
T1 - Covariance sketching
AU - Dasarathy, Gautam
AU - Shah, Parikshit
AU - Bhaskar, Badri Narayan
AU - Nowak, Robert
PY - 2012
Y1 - 2012
N2 - Learning covariance matrices from high-dimensional data is an important problem that has received a lot of attention recently. We are particularly interested in the high-dimensional setting, where the number of samples one has access to is fewer than the number of variates. Fortunately, in many applications of interest, the underlying covariance matrix is sparse and hence has limited degrees of freedom. In most existing work however, it is assumed that one can obtain samples of all the variates simultaneously. This could be very expensive or physically infeasible in some applications. As a means of overcoming this limitation, we propose a new procedure that 'pools' the covariates into a small number of groups and then samples each pooled group. We show that in certain cases it is possible to recover the covariance matrix from the pooled samples using an efficient convex optimization program, and so we call the procedure 'covariance sketching'.
AB - Learning covariance matrices from high-dimensional data is an important problem that has received a lot of attention recently. We are particularly interested in the high-dimensional setting, where the number of samples one has access to is fewer than the number of variates. Fortunately, in many applications of interest, the underlying covariance matrix is sparse and hence has limited degrees of freedom. In most existing work however, it is assumed that one can obtain samples of all the variates simultaneously. This could be very expensive or physically infeasible in some applications. As a means of overcoming this limitation, we propose a new procedure that 'pools' the covariates into a small number of groups and then samples each pooled group. We show that in certain cases it is possible to recover the covariance matrix from the pooled samples using an efficient convex optimization program, and so we call the procedure 'covariance sketching'.
UR - http://www.scopus.com/inward/record.url?scp=84875697125&partnerID=8YFLogxK
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U2 - 10.1109/Allerton.2012.6483331
DO - 10.1109/Allerton.2012.6483331
M3 - Conference contribution
AN - SCOPUS:84875697125
SN - 9781467345385
T3 - 2012 50th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2012
SP - 1026
EP - 1033
BT - 2012 50th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2012
T2 - 2012 50th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2012
Y2 - 1 October 2012 through 5 October 2012
ER -