메뉴 건너뛰기




Volumn , Issue , 2013, Pages 379-388

Efficient algorithms for selecting features with arbitrary group constraints via group lasso

Author keywords

exclusive lasso; feature group constraint; feature selection; group lasso; lasso

Indexed keywords

ACCELERATED PROXIMAL GRADIENT METHODS; CLASSIFICATION TASKS; DIFFERENT STRUCTURE; EXCLUSIVE LASSO; FEATURE GROUPS; GROUP CONSTRAINTS; GROUP LASSOS; LASSO;

EID: 84896861058     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2013.168     Document Type: Conference Paper
Times cited : (14)

References (42)
  • 2
    • 84896859134 scopus 로고    scopus 로고
    • Structured sparsity through convex optimization
    • abs/1109.2397
    • F. Bach, R. Jenatton, J. Mairal, and G. Obozinski. Structured sparsity through convex optimization. CoRR, abs/1109.2397, 2011.
    • (2011) CoRR
    • Bach, F.1    Jenatton, R.2    Mairal, J.3    Obozinski, G.4
  • 3
    • 46249088758 scopus 로고    scopus 로고
    • Consistency of the group lasso and multiple kernel learning
    • F. R. Bach. Consistency of the group lasso and multiple kernel learning. Journal of Machine Learning Research, 9:1179-1225, 2008.
    • (2008) Journal of Machine Learning Research , vol.9 , pp. 1179-1225
    • Bach, F.R.1
  • 4
    • 85014561619 scopus 로고    scopus 로고
    • A fast iterative shrinkagethresholding algorithm for linear inverse problems
    • A. Beck and M. Teboulle. A fast iterative shrinkagethresholding 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
  • 5
    • 85014561619 scopus 로고    scopus 로고
    • A fast iterative shrinkagethresholding algorithm for linear inverse problems
    • A. Beck and M. Teboulle. A fast iterative shrinkagethresholding algorithm for linear inverse problems. SIAM J. Imaging Sciences, 2(1):183-202, 2009.
    • (2009) SIAM J. Imaging Sciences , vol.2 , Issue.1 , pp. 183-202
    • Beck, A.1    Teboulle, M.2
  • 6
    • 80051762104 scopus 로고    scopus 로고
    • Distributed optimization and statistical learning via the alternating direction method of multipliers
    • S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine Learning, 3(1):1-122, 2011.
    • (2011) Foundations and Trends in Machine Learning , vol.3 , Issue.1 , pp. 1-122
    • Boyd, S.1    Parikh, N.2    Chu, E.3    Peleato, B.4    Eckstein, J.5
  • 7
    • 84858788674 scopus 로고    scopus 로고
    • Sparse signal recovery using markov random fields
    • V. Cevher, M. F. Duarte, C. Hegde, and R. G. Baraniuk. Sparse signal recovery using markov random fields. In NIPS, pages 257-264, 2008.
    • (2008) NIPS , pp. 257-264
    • Cevher, V.1    Duarte, M.F.2    Hegde, C.3    Baraniuk, R.G.4
  • 8
    • 51449111119 scopus 로고    scopus 로고
    • Iteratively reweighted algorithms for compressive sensing
    • R. Chartrand and W. Yin. Iteratively reweighted algorithms for compressive sensing. In ICASSP, pages 3869-3872, 2008.
    • (2008) ICASSP , pp. 3869-3872
    • Chartrand, R.1    Yin, W.2
  • 9
    • 84896841059 scopus 로고    scopus 로고
    • Learning with forest sparsity
    • abs/1211.4657
    • C. Chen, Y. Li, and J. Huang. Learning with forest sparsity. CoRR, abs/1211.4657, 2012.
    • (2012) CoRR
    • Chen, C.1    Li, Y.2    Huang, J.3
  • 10
    • 80053139009 scopus 로고    scopus 로고
    • Smoothing proximal gradient method for general structured sparse learning
    • X. Chen, Q. Lin, S. Kim, J. Carbonell, and E. Xing. Smoothing proximal gradient method for general structured sparse learning. In UAI, pages 105-114, 2011.
    • (2011) UAI , pp. 105-114
    • Chen, X.1    Lin, Q.2    Kim, S.3    Carbonell, J.4    Xing, E.5
  • 12
    • 52049096603 scopus 로고    scopus 로고
    • Iteratively re-weighted least squares minimization: Proof of faster than linear rate for sparse recovery
    • I. Daubechies, R. DeVore, M. Fornasier, and C. S. Güntürk. Iteratively re-weighted least squares minimization: Proof of faster than linear rate for sparse recovery. In CISS, pages 26-29, 2008.
    • (2008) CISS , pp. 26-29
    • Daubechies, I.1    Devore, R.2    Fornasier, M.3    Güntürk, C.S.4
  • 13
    • 84867615673 scopus 로고    scopus 로고
    • Nonnegative matrix factorization using a robust error function
    • C. H. Q. Ding and D. Kong. Nonnegative matrix factorization using a robust error function. In ICASSP, pages 2033-2036, 2012.
    • (2012) ICASSP , pp. 2033-2036
    • Ding, C.H.Q.1    Kong, D.2
  • 15
    • 79960138168 scopus 로고    scopus 로고
    • Nonparametric independence screening in sparse ultra-high dimensional additive models
    • J. Fan, Y. Feng, and R. Song. Nonparametric independence screening in sparse ultra-high dimensional additive models. J. Amer. Statist. Assoc., 106:544-557, 2011.
    • (2011) J. Amer. Statist. Assoc. , vol.106 , pp. 544-557
    • Fan, J.1    Feng, Y.2    Song, R.3
  • 16
    • 84896845680 scopus 로고    scopus 로고
    • Variable selection in nonparametric additive models
    • J. Huang, J. L. Horowitz, and F. Wei. Variable selection in nonparametric additive models. Technical report, 2010.
    • (2010) Technical Report
    • Huang, J.1    Horowitz, J.L.2    Wei, F.3
  • 18
    • 71149113559 scopus 로고    scopus 로고
    • Group lasso with overlap and graph lasso
    • L. Jacob, G. Obozinski, and J.-p. Vert. Group lasso with overlap and graph lasso. In ICML, page 55, 2009.
    • (2009) ICML , pp. 55
    • Jacob, L.1    Obozinski, G.2    Vert, J.-P.3
  • 19
    • 70049092408 scopus 로고    scopus 로고
    • Structured variable selection with sparsity-inducing norms
    • arXiv:0904.3523
    • R. Jenatton, J. Audibert, and F. Bach. Structured variable selection with sparsity-inducing norms. Technical report, arXiv:0904.3523, 2009.
    • (2009) Technical Report
    • Jenatton, R.1    Audibert, J.2    Bach, F.3
  • 20
    • 77956506018 scopus 로고    scopus 로고
    • Proximal methods for sparse hierarchical dictionary learning
    • R. Jenatton, J. Mairal, G. Obozinski, and F. Bach. Proximal methods for sparse hierarchical dictionary learning. In ICML, 2010.
    • (2010) ICML
    • Jenatton, R.1    Mairal, J.2    Obozinski, G.3    Bach, F.4
  • 21
    • 70450177775 scopus 로고    scopus 로고
    • Learning invariant features through topographic filter maps
    • K. Kavukcuoglu, M. Ranzato, R. Fergus, and Y. LeCun. Learning invariant features through topographic filter maps. In CVPR, pages 1605-1612, 2009.
    • (2009) CVPR , pp. 1605-1612
    • Kavukcuoglu, K.1    Ranzato, M.2    Fergus, R.3    Lecun, Y.4
  • 22
    • 66349089385 scopus 로고    scopus 로고
    • A multivariate regression approach to association analysis of a quantitative trait network
    • S. Kim, K. Sohn, and E. Xing. A multivariate regression approach to association analysis of a quantitative trait network. Bioinformatics, 25(12):204-212, 2009.
    • (2009) Bioinformatics , vol.25 , Issue.12 , pp. 204-212
    • Kim, S.1    Sohn, K.2    Xing, E.3
  • 23
    • 77956548668 scopus 로고    scopus 로고
    • Tree-guided group lasso for multi-task regression with structured sparsity
    • S. Kim and E. Xing. Tree-guided group lasso for multi-task regression with structured sparsity. In ICML, 2010.
    • (2010) ICML
    • Kim, S.1    Xing, E.2
  • 24
    • 84867132501 scopus 로고    scopus 로고
    • An iterative locally linear embedding algorithm
    • D. Kong and C. H. Q. Ding. An iterative locally linear embedding algorithm. In ICML, 2012.
    • (2012) ICML
    • Kong, D.1    Ding, C.H.Q.2
  • 25
    • 83055187059 scopus 로고    scopus 로고
    • Robust nonnegative matrix factorization using 121-norm
    • D. Kong, C. H. Q. Ding, and H. Huang. Robust nonnegative matrix factorization using 121-norm. In CIKM, pages 673-682, 2011.
    • (2011) CIKM , pp. 673-682
    • Kong, D.1    Ding, C.H.Q.2    Huang, H.3
  • 26
    • 84866673280 scopus 로고    scopus 로고
    • Multi-label relieff and f-statistic feature selections for image annotation
    • D. Kong, C. H. Q. Ding, H. Huang, and H. Zhao. Multi-label relieff and f-statistic feature selections for image annotation. In CVPR, pages 2352-2359, 2012.
    • (2012) CVPR , pp. 2352-2359
    • Kong, D.1    Ding, C.H.Q.2    Huang, H.3    Zhao, H.4
  • 27
    • 84886493829 scopus 로고    scopus 로고
    • Minimal shrinkage for noisy data recovery using schatten-p norm objective
    • D. Kong, M. Zhang, and C. H. Q. Ding. Minimal shrinkage for noisy data recovery using schatten-p norm objective. In ECML/PKDD, pages 177-193, 2013.
    • (2013) ECML/PKDD , pp. 177-193
    • Kong, D.1    Zhang, M.2    Ding, C.H.Q.3
  • 28
    • 80053145416 scopus 로고    scopus 로고
    • Multi-task feature learning via efficient l2,1-norm minimization
    • J. Liu, S. Ji, and J. Ye. Multi-task feature learning via efficient l2,1-norm minimization. In UAI, pages 339-348, 2009.
    • (2009) UAI , pp. 339-348
    • Liu, J.1    Ji, S.2    Ye, J.3
  • 29
    • 85161968806 scopus 로고    scopus 로고
    • Moreau-yosida regularization for grouped tree structure learning
    • J. Liu and J. Ye. Moreau-yosida regularization for grouped tree structure learning. In NIPS, pages 1459-1467, 2010.
    • (2010) NIPS , pp. 1459-1467
    • Liu, J.1    Ye, J.2
  • 31
    • 80055058907 scopus 로고    scopus 로고
    • Gradient methods for minimizing composite objective function
    • Y. Nesterov. Gradient methods for minimizing composite objective function. ECORE Discussion Paper, 2007.
    • (2007) ECORE Discussion Paper
    • Nesterov, Y.1
  • 33
    • 84856264699 scopus 로고    scopus 로고
    • Convex approaches to model wavelet sparsity patterns
    • N. S. Rao, R. D. Nowak, S. Wright, and N. Kingsbury. Convex approaches to model wavelet sparsity patterns. In ICIP, pages 1917-1920, 2011.
    • (2011) ICIP , pp. 1917-1920
    • Rao, N.S.1    Nowak, R.D.2    Wright, S.3    Kingsbury, N.4
  • 35
    • 27344435774 scopus 로고    scopus 로고
    • Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles
    • A. Subramanian, p. Tamayo, and et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43):15545-15550, 2005.
    • (2005) Proceedings of the National Academy of Sciences , vol.102 , Issue.43 , pp. 15545-15550
    • Subramanian, A.1    Tamayo, P.2
  • 38
    • 0037137519 scopus 로고    scopus 로고
    • A gene-expression signature as a predictor of survival in breast cancer
    • M. Vijver, Y. He, and et al. A gene-expression signature as a predictor of survival in breast cancer. New England Journal of Medicine, 347(25), 2002.
    • (2002) New England Journal of Medicine , vol.347 , Issue.25
    • Vijver, M.1    He, Y.2
  • 39
    • 84867131829 scopus 로고    scopus 로고
    • Group sparse additive models
    • J. Yin, X. Chen, and E. p. Xing. Group sparse additive models. In ICML, 2012.
    • (2012) ICML
    • Yin, J.1    Chen, X.2    Xing, E.P.3
  • 40
    • 85162375080 scopus 로고    scopus 로고
    • Efficient methods for overlapping group lasso
    • L. Yuan, J. Liu, and J. Ye. Efficient methods for overlapping group lasso. In NIPS, pages 352-360, 2011.
    • (2011) NIPS , pp. 352-360
    • Yuan, L.1    Liu, J.2    Ye, J.3
  • 41
    • 33645035051 scopus 로고    scopus 로고
    • Model selection and estimation in regression with grouped variables
    • M. Yuan and Y. Lin. Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society, Series B, 68:49-67, 2006.
    • (2006) Journal of the Royal Statistical Society, Series B , vol.68 , pp. 49-67
    • Yuan, M.1    Lin, Y.2
  • 42
    • 69949155103 scopus 로고    scopus 로고
    • D the composite absolute penalties family for grouped and hierarchical variable selection
    • p. Zhao, G. Rocha, and B. Yu. D 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.