Corpus ID: 7226523

Efficient Structured Matrix Rank Minimization

@article{Yu2014EfficientSM,
  title={Efficient Structured Matrix Rank Minimization},
  author={Adams Wei Yu and Wanli Ma and Y. Yu and J. Carbonell and S. Sra},
  journal={ArXiv},
  year={2014},
  volume={abs/1509.02447}
}
  • Adams Wei Yu, Wanli Ma, +2 authors S. Sra
  • Published 2014
  • Mathematics, Computer Science
  • ArXiv
  • We study the problem of finding structured low-rank matrices using nuclear norm regularization where the structure is encoded by a linear map. In contrast to most known approaches for linearly structured rank minimization, we do not (a) use the full SVD, nor (b) resort to augmented Lagrangian techniques, nor (c) solve linear systems per iteration. Instead, we formulate the problem differently so that it is amenable to a generalized conditional gradient method, which results in a practical… CONTINUE READING
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    References

    SHOWING 1-10 OF 32 REFERENCES
    Factorization Approach to Structured Low-Rank Approximation with Applications
    • 21
    • PDF
    Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
    • 2,922
    • PDF
    Revisiting Frank-Wolfe: Projection-Free Sparse Convex Optimization
    • M. Jaggi
    • Mathematics, Computer Science
    • ICML
    • 2013
    • 772
    • PDF
    Accelerated Training for Matrix-norm Regularization: A Boosting Approach
    • 91
    • PDF
    Hankel Matrix Rank Minimization with Applications to System Identification and Realization
    • 326
    • Highly Influential
    • PDF
    Interior-Point Method for Nuclear Norm Approximation with Application to System Identification
    • 417
    • PDF
    The Power of Convex Relaxation: Near-Optimal Matrix Completion
    • E. Candès, T. Tao
    • Mathematics, Computer Science
    • IEEE Transactions on Information Theory
    • 2010
    • 1,649
    • PDF
    Spectral Compressed Sensing via Structured Matrix Completion
    • 28
    • Highly Influential
    • PDF
    Structured low-rank approximation and its applications
    • 194
    • PDF