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This is uses the D-trace Loss (which is akin to a least squares type loss) instead of the log-likelihood loss to estimate sparse inverse covariance.
It most closely resembles the graphical lasso (produces a positive definite, symmetric, and sparse inverse covariance estimate) but has good & different incoherence/irrepresentability conditions for exact recovery (possibly but not yet provably weaker).
Probably not a priority for the next scikitquic milestone, but personally very useful/critical to me, as I need a variant of this for work. Also no good implementations exist yet in python for this.
The text was updated successfully, but these errors were encountered:
This is uses the D-trace Loss (which is akin to a least squares type loss) instead of the log-likelihood loss to estimate sparse inverse covariance.
It most closely resembles the graphical lasso (produces a positive definite, symmetric, and sparse inverse covariance estimate) but has good & different incoherence/irrepresentability conditions for exact recovery (possibly but not yet provably weaker).
Probably not a priority for the next scikitquic milestone, but personally very useful/critical to me, as I need a variant of this for work. Also no good implementations exist yet in python for this.
The text was updated successfully, but these errors were encountered: