Add to ORKGWe address some theoretical guarantees for Schatten-p quasi-norm minimization (p ∈ (0, 1]) in recovering low-rank matrices from compressed linear measurements. Firstly, using null space properties of the measuring operator, we provide a sufficient condition for exact recovery of low-rank matrices. This condition guarantees unique recovery of matrices of ranks equal or larger than what is guaranteed by nuclear norm minimization. Secondly, this sufficient condition leads to a theorem proving that all restricted isometry property (RIP) based sufficient conditions for `p quasi-norm minimization generalize to Schatten-p quasi-norm minimization. Based on this theorem, we provide a few RIP based recovery conditions
Nuclear norm minimization (NNM) has recently gained attention for its use in rank minimization probl...
The Schatten-p quasi-norm with p ∈ (0, 1) has recently gained considerable atten- tion in various lo...
The Schatten-p quasi-norm with p ∈ (0, 1) has recently gained considerable atten- tion in various lo...
The problem of recovering a low-rank matrix consistent with noisy linear measurements is a fundament...
This paper presents several novel theoretical results regarding the recovery of a low-rank matrix f...
This paper presents several novel theoretical results regarding the recovery of a low-rank matrix f...
Recovering sparse vectors and low-rank matrices from noisy linear measurements has been the focus of...
Nuclear norm minimization (NNM) has recently gained significant attention for its use in rank minimi...
Recovering sparse vectors and low-rank matrices from noisy linear measurements has been the focus of...
Nuclear norm minimization (NNM) has recently gained significant attention for its use in rank minimi...
This paper establishes a sharp condition on the restricted isometry property (RIP) for both the spar...
This paper establishes a sharp condition on the restricted isometry property (RIP) for both the spar...
This paper establishes a sharp condition on the restricted isometry property (RIP) for both the spar...
As an emerging machine learning and information re-trieval technique, the matrix completion has been...
Nuclear norm minimization (NNM) has recently gained attention for its use in rank minimization probl...
Nuclear norm minimization (NNM) has recently gained attention for its use in rank minimization probl...
The Schatten-p quasi-norm with p ∈ (0, 1) has recently gained considerable atten- tion in various lo...
The Schatten-p quasi-norm with p ∈ (0, 1) has recently gained considerable atten- tion in various lo...
The problem of recovering a low-rank matrix consistent with noisy linear measurements is a fundament...
This paper presents several novel theoretical results regarding the recovery of a low-rank matrix f...
This paper presents several novel theoretical results regarding the recovery of a low-rank matrix f...
Recovering sparse vectors and low-rank matrices from noisy linear measurements has been the focus of...
Nuclear norm minimization (NNM) has recently gained significant attention for its use in rank minimi...
Recovering sparse vectors and low-rank matrices from noisy linear measurements has been the focus of...
Nuclear norm minimization (NNM) has recently gained significant attention for its use in rank minimi...
This paper establishes a sharp condition on the restricted isometry property (RIP) for both the spar...
This paper establishes a sharp condition on the restricted isometry property (RIP) for both the spar...
This paper establishes a sharp condition on the restricted isometry property (RIP) for both the spar...
As an emerging machine learning and information re-trieval technique, the matrix completion has been...
Nuclear norm minimization (NNM) has recently gained attention for its use in rank minimization probl...
Nuclear norm minimization (NNM) has recently gained attention for its use in rank minimization probl...
The Schatten-p quasi-norm with p ∈ (0, 1) has recently gained considerable atten- tion in various lo...
The Schatten-p quasi-norm with p ∈ (0, 1) has recently gained considerable atten- tion in various lo...