메뉴 건너뛰기




Volumn 35, Issue , 2014, Pages 779-806

New algorithms for learning incoherent and overcomplete dictionaries

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; EDGE DETECTION; LEARNING SYSTEMS; POLYNOMIAL APPROXIMATION; RECOVERY; SIGNAL PROCESSING; STOCHASTIC SYSTEMS;

EID: 84939615428     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper
Times cited : (117)

References (53)
  • 4
    • 33750383209 scopus 로고    scopus 로고
    • K-svd: An algorithm for designing overcomplete dictionaries for sparse representation
    • M. Aharon, M. Elad, and A. Bruckstein. K-svd: An algorithm for designing overcomplete dictionaries for sparse representation. In IEEE Trans. on Signal Processing, pages 4311-4322, 2006.
    • (2006) IEEE Trans. on Signal Processing , pp. 4311-4322
    • Aharon, M.1    Elad, M.2    Bruckstein, A.3
  • 5
    • 38249009255 scopus 로고
    • Piercing convex sets and the hadwigder debrunner (p, q)-problem
    • N. Alon and D. Kleitman. Piercing convex sets and the hadwigder debrunner (p, q)-problem. In Advances in Mathematics, pages 103-112, 1992.
    • (1992) Advances in Mathematics , pp. 103-112
    • Alon, N.1    Kleitman, D.2
  • 6
    • 84877771041 scopus 로고    scopus 로고
    • A spectral algorithm for latent dirichlet allocation
    • A. Anandkumar, D. Foster, D. Hsu, S. Kakade, and Y. Liu. A spectral algorithm for latent dirichlet allocation. In NIPS, pages 926-934, 2012.
    • (2012) NIPS , pp. 926-934
    • Anandkumar, A.1    Foster, D.2    Hsu, D.3    Kakade, S.4    Liu, Y.5
  • 7
    • 84862609231 scopus 로고    scopus 로고
    • Computing a nonnegative matrix factorization - Provably
    • S. Arora, R. Ge, R. Kannan, and A. Moitra. Computing a nonnegative matrix factorization - provably. In STOC, pages 145-162, 2012a.
    • (2012) STOC , pp. 145-162
    • Arora, S.1    Ge, R.2    Kannan, R.3    Moitra, A.4
  • 8
    • 84871960604 scopus 로고    scopus 로고
    • Learning topic models - Going beyond svd
    • S. Arora, R. Ge, and A. Moitra. Learning topic models - going beyond svd. In FOCS, pages 1-10, 2012b.
    • (2012) FOCS , pp. 1-10
    • Arora, S.1    Ge, R.2    Moitra, A.3
  • 9
    • 84863507265 scopus 로고    scopus 로고
    • Finding overlapping communities in social networks: Towards a rigorous approach
    • S. Arora, R. Ge, S. Sachdeva, and G. Schoenebeck. Finding overlapping communities in social networks: Towards a rigorous approach. In EC, 2012c.
    • (2012) EC
    • Arora, S.1    Ge, R.2    Sachdeva, S.3    Schoenebeck, G.4
  • 12
    • 78751519918 scopus 로고    scopus 로고
    • Polynomial learning of distribution families
    • M. Belkin and K. Sinha. Polynomial learning of distribution families. In FOCS, pages 103-112, 2010.
    • (2010) FOCS , pp. 103-112
    • Belkin, M.1    Sinha, K.2
  • 14
    • 33745604236 scopus 로고    scopus 로고
    • Stable signal recovery from incomplete and inaccurate measurements
    • E. Candes, J. Romberg, and T. Tao. Stable signal recovery from incomplete and inaccurate measurements. In Communications of Pure and Applied Math, pages 1207-1223, 2006.
    • (2006) Communications of Pure and Applied Math , pp. 1207-1223
    • Candes, E.1    Romberg, J.2    Tao, T.3
  • 15
    • 0028416938 scopus 로고
    • Independent component analysis: A new concept?
    • P. Comon. Independent component analysis: A new concept? In Signal Processing, pages 287-314, 1994.
    • (1994) Signal Processing , pp. 287-314
    • Comon, P.1
  • 17
    • 0037418225 scopus 로고    scopus 로고
    • Optimally sparse representation in general (non-orthogonal) dictionaries via l1-minimization
    • D. Donoho and M. Elad. Optimally sparse representation in general (non-orthogonal) dictionaries via l1-minimization. In PNAS, pages 2197-2202, 2003.
    • (2003) PNAS , pp. 2197-2202
    • Donoho, D.1    Elad, M.2
  • 18
    • 0035504028 scopus 로고    scopus 로고
    • Uncertainty principles and ideal atomic decomposition
    • D. Donoho and X. Huo. Uncertainty principles and ideal atomic decomposition. In IEEE Trans. on Information Theory, pages 2845-2862, 1999.
    • (1999) IEEE Trans. on Information Theory , pp. 2845-2862
    • Donoho, D.1    Huo, X.2
  • 19
    • 0001616908 scopus 로고    scopus 로고
    • Uncertainty principles and signal recovery
    • D. Donoho and P. Stark. Uncertainty principles and signal recovery. In SIAM J. on Appl. Math, pages 906-931, 1999.
    • (1999) SIAM J. on Appl. Math , pp. 906-931
    • Donoho, D.1    Stark, P.2
  • 21
    • 33751379736 scopus 로고    scopus 로고
    • Image denoising via sparse and redundant representations over learned dictionaries
    • M. Elad and M. Aharon. Image denoising via sparse and redundant representations over learned dictionaries. In IEEE Trans. on Signal Processing, pages 3736-3745, 2006.
    • (2006) IEEE Trans. on Signal Processing , pp. 3736-3745
    • Elad, M.1    Aharon, M.2
  • 22
    • 0032627073 scopus 로고    scopus 로고
    • Method of optimal directions for frame design
    • K. Engan, S. Aase, and J. Hakon-Husoy. Method of optimal directions for frame design. In ICASSP, pages 2443-2446, 1999.
    • (1999) ICASSP , pp. 2443-2446
    • Engan, K.1    Aase, S.2    Hakon-Husoy, J.3
  • 23
    • 0030422226 scopus 로고    scopus 로고
    • Learning linear transformations
    • A. Frieze, M. Jerrum, and R. Kannan. Learning linear transformations. In FOCS, pages 359-368, 1996.
    • (1996) FOCS , pp. 359-368
    • Frieze, A.1    Jerrum, M.2    Kannan, R.3
  • 25
    • 0037740006 scopus 로고    scopus 로고
    • Approximation of functions over redundant dictionaries using coherence
    • A. Gilbert, S. Muthukrishnan, and M. Strauss. Approximation of functions over redundant dictionaries using coherence. In SODA, 2003.
    • (2003) SODA
    • Gilbert, A.1    Muthukrishnan, S.2    Strauss, M.3
  • 27
    • 84877773068 scopus 로고    scopus 로고
    • Large-scale feature learning with spike-and-slab sparse coding
    • I. J. Goodfellow, A. Courville, and Y. Bengio. Large-scale feature learning with spike-and-slab sparse coding. In ICML, pages 718-726, 2012.
    • (2012) ICML , pp. 718-726
    • Goodfellow, I.J.1    Courville, A.2    Bengio, Y.3
  • 31
    • 0038126290 scopus 로고
    • A bound on tail probabilities for quadratic forms in independent random variables
    • D. Hanson and F. Wright. A bound on tail probabilities for quadratic forms in independent random variables. In Annals of Math. Stat., pages 1079-1083, 1971.
    • (1971) Annals of Math. Stat. , pp. 1079-1083
    • Hanson, D.1    Wright, F.2
  • 34
    • 84879826396 scopus 로고    scopus 로고
    • Low rank matrix completion using alternating minimization
    • P. Jain, P. Netrapalli, and S. Sanghavi. Low rank matrix completion using alternating minimization. In STOC, pages 665-674, 2013.
    • (2013) STOC , pp. 665-674
    • Jain, P.1    Netrapalli, P.2    Sanghavi, S.3
  • 35
    • 70049083257 scopus 로고    scopus 로고
    • Fast inference in sparse coding algorithms with applications to object recognition
    • Tech Report
    • K. Kavukcuoglu, M. Ranzato, and Y. Le Cun. Fast inference in sparse coding algorithms with applications to object recognition. In NYU Tech Report, 2008.
    • (2008) NYU
    • Kavukcuoglu, K.1    Ranzato, M.2    Le Cun, Y.3
  • 37
    • 34249802620 scopus 로고    scopus 로고
    • Fourth-order cumulant-based blind identification of underdetermined mixtures
    • L. De Lathauwer, J. Castaing, and J. Cardoso. Fourth-order cumulant-based blind identification of underdetermined mixtures. In IEEE Trans. on Signal Processing, pages 2965-2973, 2007.
    • (2007) IEEE Trans. on Signal Processing , pp. 2965-2973
    • De Lathauwer, L.1    Castaing, J.2    Cardoso, J.3
  • 38
    • 85147175076 scopus 로고    scopus 로고
    • Efficient sparse coding algorithms
    • H. Lee, A. Battle, R. Raina, and A. Ng. Efficient sparse coding algorithms. In NIPS, 2006.
    • (2006) NIPS
    • Lee, H.1    Battle, A.2    Raina, R.3    Ng, A.4
  • 39
    • 0034133184 scopus 로고    scopus 로고
    • Learning overcomplete representations
    • M. Lewicki and T. Sejnowski. Learning overcomplete representations. In Neural Computation, pages 337-365, 2000.
    • (2000) Neural Computation , pp. 337-365
    • Lewicki, M.1    Sejnowski, T.2
  • 40
    • 77952722803 scopus 로고    scopus 로고
    • Discriminative sparse image models for class-specific edge detection and image interpretation
    • J. Mairal, M. Leordeanu, F. Bach, M. Herbert, and J. Ponce. Discriminative sparse image models for class-specific edge detection and image interpretation. In ECCV, 2008.
    • (2008) ECCV
    • Mairal, J.1    Leordeanu, M.2    Bach, F.3    Herbert, M.4    Ponce, J.5
  • 43
    • 78751527010 scopus 로고    scopus 로고
    • Setting the polynomial learnability of mixtures of gaussians
    • A. Moitra and G. Valiant. Setting the polynomial learnability of mixtures of gaussians. In FOCS, pages 93-102, 2010.
    • (2010) FOCS , pp. 93-102
    • Moitra, A.1    Valiant, G.2
  • 44
    • 0030779611 scopus 로고    scopus 로고
    • Sparse coding with an overcomplete basis set: A strategy employed by v1?
    • B. Olshausen and B. Field. Sparse coding with an overcomplete basis set: A strategy employed by v1? In Vision Research, pages 3331-3325, 1997.
    • (1997) Vision Research , pp. 3331-3325
    • Olshausen, B.1    Field, B.2
  • 46
    • 70049094447 scopus 로고    scopus 로고
    • Sparse feature learning for deep belief networks
    • M. Ranzato, Y. Boureau, and Y. Le Cun. Sparse feature learning for deep belief networks. In NIPS, 2007.
    • (2007) NIPS
    • Ranzato, M.1    Boureau, Y.2    Le Cun, Y.3
  • 47
    • 0033541884 scopus 로고    scopus 로고
    • Random vectors in the isotropic position
    • M. Rudelson. Random vectors in the isotropic position. In J. of Functional Analysis, pages 60-72, 1999.
    • (1999) J. of Functional Analysis , pp. 60-72
    • Rudelson, M.1
  • 49
    • 5444237123 scopus 로고    scopus 로고
    • Greed is good: Algorithmic results for sparse approximation
    • J. Tropp. Greed is good: Algorithmic results for sparse approximation. In IEEE Transactions on Information Theory, pages 2231-2242, 2004.
    • (2004) IEEE Transactions on Information Theory , pp. 2231-2242
    • Tropp, J.1
  • 51
    • 0002790288 scopus 로고
    • Perturbation bounds in connection with singular value decompositions
    • P. Wedin. Perturbation bounds in connection with singular value decompositions. In BIT, pages 99-111, 1972.
    • (1972) BIT , pp. 99-111
    • Wedin, P.1
  • 52
    • 51949105499 scopus 로고    scopus 로고
    • Image super-resolution as sparse representation of raw image patches
    • J. Yang, J. Wright, T. Huong, and Y. Ma. Image super-resolution as sparse representation of raw image patches. In CVPR, 2008.
    • (2008) CVPR
    • Yang, J.1    Wright, J.2    Huong, T.3    Ma, Y.4
  • 53
    • 84877607371 scopus 로고    scopus 로고
    • Truncated power method for sparse eigenvalue problems
    • X. Yuan and T. Zhang. Truncated power method for sparse eigenvalue problems. In Journal of Machine Learning Research, pages 899-925, 2013.
    • (2013) Journal of Machine Learning Research , pp. 899-925
    • Yuan, X.1    Zhang, T.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.