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Volumn 382, Issue , 2009, Pages

An efficient projection for l1,∞ regularization

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE ANNOTATION; PROJECTED GRADIENT; SPARSE SOLUTIONS;

EID: 70049097965     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1553374.1553484     Document Type: Conference Paper
Times cited : (38)

References (21)
  • 4
    • 29144489844 scopus 로고    scopus 로고
    • For most large underdetermined systems of linear equations the minimal l1-norm solution is also the sparsest solution
    • Statistics Dept, Stanford University
    • Donoho, D. (2004). For most large underdetermined systems of linear equations the minimal l1-norm solution is also the sparsest solution. (Technical Report). Statistics Dept., Stanford University.
    • (2004) Technical Report
    • Donoho, D.1
  • 6
    • 34247576789 scopus 로고    scopus 로고
    • The pyramid match kernel: Efficient learning with sets of features
    • Grauman, K., & Darrell, T. (2008). The pyramid match kernel: Efficient learning with sets of features. Journal of Machine Learning Research, 8, 725-760.
    • (2008) Journal of Machine Learning Research , vol.8 , pp. 725-760
    • Grauman, K.1    Darrell, T.2
  • 9
    • 14344249889 scopus 로고    scopus 로고
    • Feature selection, l1 vs. l2 regularization, and rotational invariance
    • Ng, A. Y. (2004). Feature selection, l1 vs. l2 regularization, and rotational invariance. Proc. of Intl. Conf. on Machine Learning.
    • (2004) Proc. of Intl. Conf. on Machine Learning
    • Ng, A.Y.1
  • 11
  • 13
    • 51949094374 scopus 로고    scopus 로고
    • Qu1attoni, A., Collins, M., & Darrell, T. (2008). Transfer learning for image classification with sparse prototype representations. Proc. of Conf. on Computer Vision and Pattern Recognition.
    • Qu1attoni, A., Collins, M., & Darrell, T. (2008). Transfer learning for image classification with sparse prototype representations. Proc. of Conf. on Computer Vision and Pattern Recognition.
  • 17
    • 34548232392 scopus 로고    scopus 로고
    • Input selection and shrinkage in multiresponse linear regression
    • Similä, T., & Tikka, J. (2007). Input selection and shrinkage in multiresponse linear regression. Computational Statistics and Data Analysis, 52, 406-422.
    • (2007) Computational Statistics and Data Analysis , vol.52 , pp. 406-422
    • Similä, T.1    Tikka, J.2
  • 18
    • 30844461481 scopus 로고    scopus 로고
    • Algorithms for simultaneous sparse approximation, part ii: Convex relaxation
    • Tropp, J. (2006). Algorithms for simultaneous sparse approximation, part ii: convex relaxation. Signal Processing (pp. 589-602).
    • (2006) Signal Processing , pp. 589-602
    • Tropp, J.1
  • 19
  • 20
    • 33645035051 scopus 로고    scopus 로고
    • Model selection and estimation in regression with grouped variables
    • Yuan, M., & Lin, Y. (2006). Model selection and estimation in regression with grouped variables. J. Royal Statistical Society Series B, 68, 49-67.
    • (2006) J. Royal Statistical Society Series B , vol.68 , pp. 49-67
    • Yuan, M.1    Lin, Y.2
  • 21
    • 1942484421 scopus 로고    scopus 로고
    • Online convex programming and generalized infinitesimal gradient ascent
    • Zinkevich, M. (2003). Online convex programming and generalized infinitesimal gradient ascent. Proc. of Intl. Conf. on Machine Learning (pp. 928-936).
    • (2003) Proc. of Intl. Conf. on Machine Learning , pp. 928-936
    • Zinkevich, M.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.