
Improved Sparse LowRank Matrix Estimation
We address the problem of estimating a sparse lowrank matrix from its n...
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Link Prediction in Graphs with Autoregressive Features
In the paper, we consider the problem of link prediction in timeevolvin...
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Smoothed Low Rank and Sparse Matrix Recovery by Iteratively Reweighted Least Squares Minimization
This work presents a general framework for solving the low rank and/or s...
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A generalization error bound for sparse and lowrank multivariate Hawkes processes
We consider the problem of unveiling the implicit network structure of u...
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Adaptive estimation of the copula correlation matrix for semiparametric elliptical copulas
We study the adaptive estimation of copula correlation matrix Σ for the ...
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Simultaneously sparse and lowrank abundance matrix estimation for hyperspectral image unmixing
In a plethora of applications dealing with inverse problems, e.g. in ima...
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A Distributed FrankWolfe Framework for Learning LowRank Matrices with the Trace Norm
We consider the problem of learning a highdimensional but lowrank matr...
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Estimation of Simultaneously Sparse and Low Rank Matrices
The paper introduces a penalized matrix estimation procedure aiming at solutions which are sparse and lowrank at the same time. Such structures arise in the context of social networks or protein interactions where underlying graphs have adjacency matrices which are blockdiagonal in the appropriate basis. We introduce a convex mixed penalty which involves ℓ_1norm and trace norm simultaneously. We obtain an oracle inequality which indicates how the two effects interact according to the nature of the target matrix. We bound generalization error in the link prediction problem. We also develop proximal descent strategies to solve the optimization problem efficiently and evaluate performance on synthetic and real data sets.
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