메뉴 건너뛰기




Volumn , Issue , 2013, Pages

Stochastic majorization-minimization algorithms for large-scale optimization

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CONVEX OPTIMIZATION; CONVEX PROGRAMMING; FUNCTIONS; GRADIENT METHODS; SIGNAL PROCESSING; STOCHASTIC SYSTEMS;

EID: 84898983087     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (132)

References (30)
  • 1
    • 85014561619 scopus 로고    scopus 로고
    • A fast iterative shrinkage-thresholding algorithm for linear inverse problems
    • A. Beck and M. Teboulle. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imaging Sci., 2(1):183-202, 2009.
    • (2009) SIAM J. Imaging Sci. , vol.2 , Issue.1 , pp. 183-202
    • Beck, A.1    Teboulle, M.2
  • 3
    • 0013309537 scopus 로고    scopus 로고
    • Online algorithms and stochastic approximations
    • David Saad, editor
    • L. Bottou. Online algorithms and stochastic approximations. In David Saad, editor, Online Learning and Neural Networks. 1998.
    • (1998) Online Learning and Neural Networks
    • Bottou, L.1
  • 4
    • 85162035281 scopus 로고    scopus 로고
    • The trade-offs of large scale learning
    • L. Bottou and O. Bousquet. The trade-offs of large scale learning. In Adv. NIPS, 2008.
    • (2008) Adv. NIPS
    • Bottou, L.1    Bousquet, O.2
  • 5
    • 66849104300 scopus 로고    scopus 로고
    • On-line expectation-maximization algorithm for latent data models
    • O. Cappé and E. Moulines. On-line expectation-maximization algorithm for latent data models. J. Roy. Stat. Soc. B, 71(3):593-613, 2009.
    • (2009) J. Roy. Stat. Soc. B , vol.71 , Issue.3 , pp. 593-613
    • Cappé, O.1    Moulines, E.2
  • 6
    • 75249102673 scopus 로고    scopus 로고
    • Efficient online and batch learning using forward backward splitting
    • J. Duchi and Y. Singer. Efficient online and batch learning using forward backward splitting. J. Mach. Learn. Res., 10:2899-2934, 2009.
    • (2009) J. Mach. Learn. Res. , vol.10 , pp. 2899-2934
    • Duchi, J.1    Singer, Y.2
  • 8
    • 70450245260 scopus 로고    scopus 로고
    • Recovering sparse signals with non-convex penalties and DC programming
    • G. Gasso, A. Rakotomamonjy, and S. Canu. Recovering sparse signals with non-convex penalties and DC programming. IEEE T. Signal Process., 57(12):4686-4698, 2009.
    • (2009) IEEE T. Signal Process. , vol.57 , Issue.12 , pp. 4686-4698
    • Gasso, G.1    Rakotomamonjy, A.2    Canu, S.3
  • 10
    • 35348918820 scopus 로고    scopus 로고
    • Logarithmic regret algorithms for online convex optimization
    • E. Hazan, A. Agarwal, and S. Kale. Logarithmic regret algorithms for online convex optimization. Mach. Learn., 69(2-3):169-192, 2007.
    • (2007) Mach. Learn. , vol.69 , Issue.2-3 , pp. 169-192
    • Hazan, E.1    Agarwal, A.2    Kale, S.3
  • 11
    • 84898979568 scopus 로고    scopus 로고
    • Beyond the regret minimization barrier: An optimal algorithm for stochastic strongly-convex optimization
    • E. Hazan and S. Kale. Beyond the regret minimization barrier: an optimal algorithm for stochastic strongly-convex optimization. In Proc. COLT, 2011.
    • (2011) Proc. COLT
    • Hazan, E.1    Kale, S.2
  • 12
    • 77956508892 scopus 로고    scopus 로고
    • Accelerated gradient methods for stochastic optimization and online learning
    • C. Hu, J. Kwok, and W. Pan. Accelerated gradient methods for stochastic optimization and online learning. In Adv. NIPS, 2009.
    • (2009) Adv. NIPS
    • Hu, C.1    Kwok, J.2    Pan, W.3
  • 14
    • 84862273593 scopus 로고    scopus 로고
    • An optimal method for stochastic composite optimization
    • G. Lan. An optimal method for stochastic composite optimization. Math. Program., 133:365-397, 2012.
    • (2012) Math. Program. , vol.133 , pp. 365-397
    • Lan, G.1
  • 15
    • 77950023906 scopus 로고    scopus 로고
    • Optimization transfer using surrogate objective functions
    • K. Lange, D.R. Hunter, and I. Yang. Optimization transfer using surrogate objective functions. J. Comput. Graph. Stat., 9(1):1-20, 2000.
    • (2000) J. Comput. Graph. Stat. , vol.9 , Issue.1 , pp. 1-20
    • Lange, K.1    Hunter, D.R.2    Yang, I.3
  • 16
    • 64149115569 scopus 로고    scopus 로고
    • Sparse online learning via truncated gradient
    • J. Langford, L. Li, and T. Zhang. Sparse online learning via truncated gradient. J. Mach. Learn. Res., 10:777-801, 2009.
    • (2009) J. Mach. Learn. Res. , vol.10 , pp. 777-801
    • Langford, J.1    Li, L.2    Zhang, T.3
  • 17
    • 84877725219 scopus 로고    scopus 로고
    • A stochastic gradient method with an exponential convergence rate for finite training sets
    • N. Le Roux, M. Schmidt, and F. Bach. A stochastic gradient method with an exponential convergence rate for finite training sets. In Adv. NIPS, 2012.
    • (2012) Adv. NIPS
    • Le Roux, N.1    Schmidt, M.2    Bach, F.3
  • 18
    • 84897534825 scopus 로고    scopus 로고
    • Optimization with first-order surrogate functions
    • arXiv: 1305.3120
    • J. Mairal. Optimization with first-order surrogate functions. In Proc. ICML, 2013. arXiv:1305.3120.
    • Proc ICML, 2013
    • Mairal, J.1
  • 19
    • 76749107542 scopus 로고    scopus 로고
    • Online learning for matrix factorization and sparse coding
    • J. Mairal, F. Bach, J. Ponce, and G. Sapiro. Online learning for matrix factorization and sparse coding. J. Mach. Learn. Res., 11:19-60, 2010.
    • (2010) J. Mach. Learn. Res. , vol.11 , pp. 19-60
    • Mairal, J.1    Bach, F.2    Ponce, J.3    Sapiro, G.4
  • 21
    • 0002788893 scopus 로고    scopus 로고
    • A view of the em algorithm that justifies incremental, sparse, and other variants
    • R.M. Neal and G.E. Hinton. A view of the EM algorithm that justifies incremental, sparse, and other variants. Learning in graphical models, 89, 1998.
    • (1998) Learning in Graphical Models , vol.89
    • Neal, R.M.1    Hinton, G.E.2
  • 22
    • 70450197241 scopus 로고    scopus 로고
    • Robust stochastic approximation approach to stochastic programming
    • A. Nemirovski, A. Juditsky, G. Lan, and A. Shapiro. Robust stochastic approximation approach to stochastic programming. SIAM J. Optimiz., 19(4):1574-1609, 2009.
    • (2009) SIAM J. Optimiz. , vol.19 , Issue.4 , pp. 1574-1609
    • Nemirovski, A.1    Juditsky, A.2    Lan, G.3    Shapiro, A.4
  • 23
  • 26
    • 71149119963 scopus 로고    scopus 로고
    • Stochastic methods for ℓ1 regularized loss minimization
    • S. Shalev-Shwartz and A. Tewari. Stochastic methods for ℓ1 regularized loss minimization. In Proc. ICML, 2009.
    • (2009) Proc. ICML
    • Shalev-Shwartz, S.1    Tewari, A.2
  • 28
    • 65749118363 scopus 로고    scopus 로고
    • Graphical models, exponential families, and variational inference
    • M.J. Wainwright and M.I. Jordan. Graphical models, exponential families, and variational inference. Found. Trends Mach. Learn., 1(1-2):1-305, 2008.
    • (2008) Found. Trends Mach. Learn. , vol.1 , Issue.1-2 , pp. 1-305
    • Wainwright, M.J.1    Jordan, M.I.2
  • 29
    • 67650178787 scopus 로고    scopus 로고
    • Sparse reconstruction by separable approximation
    • S. Wright, R. Nowak, and M. Figueiredo. Sparse reconstruction by separable approximation. IEEE T. Signal Process., 57(7):2479-2493, 2009.
    • (2009) IEEE T. Signal Process. , vol.57 , Issue.7 , pp. 2479-2493
    • Wright, S.1    Nowak, R.2    Figueiredo, M.3
  • 30
    • 78649396336 scopus 로고    scopus 로고
    • Dual averaging methods for regularized stochastic learning and online optimization
    • L. Xiao. Dual averaging methods for regularized stochastic learning and online optimization. J. Mach. Learn. Res., 11:2543-2596, 2010.
    • (2010) J. Mach. Learn. Res. , vol.11 , pp. 2543-2596
    • Xiao, L.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.