Self-Supervised Learning for Covariance Estimation
We consider the use of deep learning for covariance estimation. We propose to globally
learn a neural network that will then be applied locally at inference time. Leveraging recent …
learn a neural network that will then be applied locally at inference time. Leveraging recent …
Joint estimation of inverse covariance matrices lying in an unknown subspace
I Soloveychik, A Wiesel - IEEE Transactions on Signal …, 2017 - ieeexplore.ieee.org
We consider the problem of joint estimation of inverse covariance matrices lying in an
unknown subspace of the linear space of symmetric matrices. We perform the estimation …
unknown subspace of the linear space of symmetric matrices. We perform the estimation …
Improved adaptive clutter suppression based on multi-look processing in heterogeneous background
It is known that the performance of adaptive clutter suppression may be reduced seriously in
a heterogeneous background, due to the lack of enough training samples to exactly obtain …
a heterogeneous background, due to the lack of enough training samples to exactly obtain …
Joint inverse covariances estimation with mutual linear structure
I Soloveychik, A Wiesel - 2015 23rd European Signal …, 2015 - ieeexplore.ieee.org
We consider the problem of joint estimation of structured inverse covariance matrices. We
assume the structure is unknown and perform the estimation using groups of measurements …
assume the structure is unknown and perform the estimation using groups of measurements …