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CDC
2008
IEEE
145views Control Systems» more  CDC 2008»
9 years 5 months ago
Necessary and sufficient conditions for success of the nuclear norm heuristic for rank minimization
Minimizing the rank of a matrix subject to constraints is a challenging problem that arises in many applications in control theory, machine learning, and discrete geometry. This c...
Benjamin Recht, Weiyu Xu, Babak Hassibi
MP
2011
9 years 21 days ago
Null space conditions and thresholds for rank minimization
Minimizing the rank of a matrix subject to constraints is a challenging problem that arises in many applications in machine learning, control theory, and discrete geometry. This c...
Benjamin Recht, Weiyu Xu, Babak Hassibi
ICASSP
2010
IEEE
9 years 6 months ago
A nullspace analysis of the nuclear norm heuristic for rank minimization
The problem of minimizing the rank of a matrix subject to linear equality constraints arises in applications in machine learning, dimensionality reduction, and control theory, and...
Krishnamurthy Dvijotham, Maryam Fazel
ICASSP
2011
IEEE
8 years 9 months ago
Improved thresholds for rank minimization
—Nuclear norm minimization (NNM) has recently gained attention for its use in rank minimization problems. In this paper, we define weak, sectional and strong recovery for NNM to...
Samet Oymak, M. Amin Khajehnejad, Babak Hassibi
CORR
2010
Springer
93views Education» more  CORR 2010»
9 years 5 months ago
Rank Awareness in Joint Sparse Recovery
In this paper we revisit the sparse multiple measurement vector (MMV) problem, where the aim is to recover a set of jointly sparse multichannel vectors from incomplete measurement...
Mike E. Davies, Yonina C. Eldar
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