메뉴 건너뛰기




Volumn 9, Issue , 2010, Pages 709-716

Convex structure learning in log-linear models: Beyond pairwise potentials

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVE SET METHODS; ARBITRARY ORDER; CONVEX STRUCTURE; DYKSTRA'S ALGORITHM; EXPONENTIAL NUMBERS; HIGHER ORDER; LOGLINEAR MODEL; OVERLAPPING GROUPS; SPECTRAL PROJECTED GRADIENT METHOD; STRUCTURE-LEARNING; TEST SETS;

EID: 80052909104     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper
Times cited : (28)

References (33)
  • 1
    • 85027388762 scopus 로고    scopus 로고
    • Exploring large feature spaces with hierarchical multiple kernel learning
    • F. Bach. Exploring large feature spaces with hierarchical multiple kernel learning. NIPS, 2008.
    • (2008) NIPS
    • Bach, F.1
  • 2
    • 41549101939 scopus 로고    scopus 로고
    • Model selection through sparse maximum likelihood estimation for multivariate Gaussian or binary data
    • O. Banerjee, L. El Ghaoui, and A. d'Aspremont. Model selection through sparse maximum likelihood estimation for multivariate Gaussian or binary data. JMLR, 2008.
    • (2008) JMLR
    • Banerjee, O.1    El Ghaoui, L.2    D'Aspremont, A.3
  • 4
    • 0000582521 scopus 로고
    • Statistical analysis of non-lattice data
    • J. Besag. Statistical analysis of non-lattice data. The Statistician, 24(3):179-195, 1975.
    • (1975) The Statistician , vol.24 , Issue.3 , pp. 179-195
    • Besag, J.1
  • 5
    • 0034345420 scopus 로고    scopus 로고
    • Nonmonotone spectral projected gradient methods on convex sets
    • E. Birgin, J. Martínez, and M. Raydan. Nonmonotone spectral projected gradient methods on convex sets. SIAM J. Optim., 10(4):1196-1211, 2000.
    • (2000) SIAM J. Optim. , vol.10 , Issue.4 , pp. 1196-1211
    • Birgin, E.1    Martínez, J.2    Raydan, M.3
  • 8
    • 0001263852 scopus 로고
    • The method of successive projection for finding a common point of convex sets
    • L. Bregman. The method of successive projection for finding a common point of convex sets. Dokl. Akad. Nauk SSSR, 162(3):487-490, 1965.
    • (1965) Dokl. Akad. Nauk SSSR , vol.162 , Issue.3 , pp. 487-490
    • Bregman, L.1
  • 9
    • 0001336448 scopus 로고
    • English translation in Soviet Math. Dokl., 6:688-692, 1965.
    • (1965) Soviet Math. Dokl. , vol.6 , pp. 688-692
  • 10
    • 85056103572 scopus 로고    scopus 로고
    • Efficient principled learning of thin junction trees
    • A. Chechetka and C. Guestrin. Efficient principled learning of thin junction trees. NIPS, 2007.
    • (2007) NIPS
    • Chechetka, A.1    Guestrin, C.2
  • 11
    • 41149099117 scopus 로고    scopus 로고
    • Penalized likelihood for sparse contingency tables with an application to full-length cDNA libraries
    • C. Dahinden, G. Parmigiani, M. Emerick, and P. Bühlmann. Penalized likelihood for sparse contingency tables with an application to full-length cDNA libraries. BMC Bioinf., 8(476), 2007.
    • (2007) BMC Bioinf. , vol.8 , Issue.476
    • Dahinden, C.1    Parmigiani, G.2    Emerick, M.3    Bühlmann, P.4
  • 12
    • 0002969298 scopus 로고
    • The rate of convergence of Dykstra's cyclic projections algorithm: The polyhedral case
    • F. Deutsch and H. Hundal. The rate of convergence of Dykstra's cyclic projections algorithm: The polyhedral case. Numer. Funct. Anal. Optim., 15(5):537-565, 1994.
    • (1994) Numer. Funct. Anal. Optim. , vol.15 , Issue.5 , pp. 537-565
    • Deutsch, F.1    Hundal, H.2
  • 13
    • 84862294973 scopus 로고    scopus 로고
    • The mode oriented stochastic search (MOSS) algorithm for log-linear models with conjugate priors
    • A. Dobra and H. Massam. The mode oriented stochastic search (MOSS) algorithm for log-linear models with conjugate priors. Technical report, University of Washington, 2008.
    • (2008) Technical Report University of Washington
    • Dobra, A.1    Massam, H.2
  • 14
    • 84948499836 scopus 로고
    • An algorithm for restricted least squares regression
    • R. Dykstra. An algorithm for restricted least squares regression. JASA, 78(384):837-842, 1983.
    • (1983) JASA , vol.78 , Issue.384 , pp. 837-842
    • Dykstra, R.1
  • 15
    • 0000130823 scopus 로고
    • A fast procedure for model search in multidimensional contingency tables
    • D. Edwards and T. Havránek. A fast procedure for model search in multidimensional contingency tables. Biometrika, 72(2):339-351, 1985.
    • (1985) Biometrika , vol.72 , Issue.2 , pp. 339-351
    • Edwards, D.1    Havránek, T.2
  • 16
    • 2542631648 scopus 로고    scopus 로고
    • Kernel methods for multilabelled classification and categorical regression problems
    • A. Elisseeff and J. Weston. Kernel methods for multilabelled classification and categorical regression problems. In NIPS, 2002.
    • (2002) NIPS
    • Elisseeff, A.1    Weston, J.2
  • 18
    • 39449126969 scopus 로고    scopus 로고
    • Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems
    • M. Figueiredo, R. Nowak, and S. Wright. Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems. IEEE J. Sel. Top. Sign. Proces., 1(4):586-597, 2007.
    • (2007) IEEE J. Sel. Top. Sign. Proces. , vol.1 , Issue.4 , pp. 586-597
    • Figueiredo, M.1    Nowak, R.2    Wright, S.3
  • 19
    • 0002549585 scopus 로고    scopus 로고
    • Eigentaste: A constant time collaborative filtering algorithm
    • K. Goldberg, T. Roeder, D. Gupta, and C. Perkins. Eigentaste: A constant time collaborative filtering algorithm. Inf. Retrieval, 4(2):133-151, 2001.
    • (2001) Inf. Retrieval , vol.4 , Issue.2 , pp. 133-151
    • Goldberg, K.1    Roeder, T.2    Gupta, D.3    Perkins, C.4
  • 20
    • 66549109770 scopus 로고    scopus 로고
    • Estimation of sparse binary pairwise Markov networks using pseudo-likelihoods
    • H. Höing and R. Tibshirani. Estimation of sparse binary pairwise Markov networks using pseudo-likelihoods. JMLR, 10:883-906, 2009.
    • (2009) JMLR , vol.10 , pp. 883-906
    • Höing, H.1    Tibshirani, R.2
  • 22
    • 85142829716 scopus 로고    scopus 로고
    • Efficient structure learning of Markov networks using l1-regularization
    • S. Lee, V. Ganapathi, and D. Koller. Efficient structure learning of Markov networks using l1-regularization. NIPS, 2006.
    • (2006) NIPS
    • Lee, S.1    Ganapathi, V.2    Koller, D.3
  • 25
    • 17644427718 scopus 로고    scopus 로고
    • Causal protein-signaling networks derived from multiparameter single-cell data
    • K. Sachs, O. Perez, D. Pe'er, D. Laufienburger, and G. Nolan. Causal protein-signaling networks derived from multiparameter single-cell data. Science, 308 (5721):523-529, 2005.
    • (2005) Science , vol.308 , Issue.5721 , pp. 523-529
    • Sachs, K.1    Perez, O.2    Pe'Er, D.3    Laufienburger, D.4    Nolan, G.5
  • 26
    • 51949118201 scopus 로고    scopus 로고
    • Structure learning in random fields for heart motion abnormality detection
    • M. Schmidt, K. Murphy, G. Fung, and R. Rosales. Structure learning in random fields for heart motion abnormality detection. CVPR, 2008.
    • (2008) CVPR
    • Schmidt, M.1    Murphy, K.2    Fung, G.3    Rosales, R.4
  • 27
    • 77955995598 scopus 로고    scopus 로고
    • Optimizing costly functions with simple constraints: A limited-memory projected quasi-Newton algorithm
    • M. Schmidt, E. van den Berg, M. Friedlander, and K. Murphy. Optimizing costly functions with simple constraints: A limited-memory projected quasi-Newton algorithm. AISTATS, 2009.
    • (2009) AISTATS
    • Schmidt, M.1    Van Den Berg, E.2    Friedlander, M.3    Murphy, K.4
  • 28
    • 65649137930 scopus 로고    scopus 로고
    • Probing the Pareto frontier for basis pursuit solutions
    • E. van den Berg and M. Friedlander. Probing the Pareto frontier for basis pursuit solutions. SIAM J. Sci. Comput., 31(2):890-912, 2008.
    • (2008) SIAM J. Sci. Comput. , vol.31 , Issue.2 , pp. 890-912
    • Van Den Berg, E.1    Friedlander, M.2
  • 29
    • 0039669502 scopus 로고
    • The Geometry of Orthogonal Spaces, volume 22 of Annals of Mathematical Studies. Princeton University Press, This is a reprint of notes first distributed in 1933
    • J. von Neumann. Functional Operators, vol. II, The Geometry of Orthogonal Spaces, volume 22 of Annals of Mathematical Studies. Princeton University Press, 1950. This is a reprint of notes first distributed in 1933.
    • (1950) Functional Operators , vol.2
    • Von Neumann, J.1
  • 30
    • 65749118363 scopus 로고    scopus 로고
    • Graphical models, exponential families, and variational inference
    • M. Wainwright and M. 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.1    Jordan, M.2
  • 31
    • 85146130171 scopus 로고    scopus 로고
    • Highdimensional graphical model selection using l1- regularized logistic regression
    • M. Wainwright, P. Ravikumar, and J. Lafferty. Highdimensional graphical model selection using l1- regularized logistic regression. NIPS, 2006.
    • (2006) NIPS
    • Wainwright, M.1    Ravikumar, P.2    Lafferty, J.3
  • 33
    • 69949155103 scopus 로고    scopus 로고
    • The composite absolute penalties family for grouped and hierarchical variable selection
    • P. Zhao, G. Rocha, and B. Yu. The composite absolute penalties family for grouped and hierarchical variable selection. Annals of Statistics, 37(6A):3468-3497, 2009.
    • (2009) Annals of Statistics , vol.37 , Issue.6 A , pp. 3468-3497
    • Zhao, P.1    Rocha, G.2    Yu, B.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.