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Volumn 1, Issue , 2009, Pages 96-107

Proximity-based anomaly detection using sparse structure learning

Author keywords

[No Author keywords available]

Indexed keywords

ANOMALY DETECTION; COLLINEARITY; CONDITIONAL DISTRIBUTION; DATA SETS; GAUSSIAN MODEL; HIGHLY-CORRELATED; INVERSE COVARIANCE; MARKOV BLANKETS; MATRIX; MULTIVARIATE DATA; PHYSICAL SENSORS; PRECISION MATRIX; REAL WORLD DATA; REFERENCE DATA; SELECTION PROCEDURES; STRUCTURE-LEARNING; TEST DATA; TIME-SERIES DATA;

EID: 72849150929     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (48)

References (26)
  • 3
    • 33749250772 scopus 로고    scopus 로고
    • Convex optimization techniques for fitting sparse Gaussian graphical models
    • Press
    • O. Banerjee, L. E. Ghaoui, and G. Natsoulis. Convex optimization techniques for fitting sparse Gaussian graphical models. In Proc. Intl. Conf. Machine Learning, pages 89-96. Press, 2006.
    • (2006) Proc. Intl. Conf. Machine Learning , pp. 89-96
    • Banerjee, O.1    Ghaoui, L.E.2    Natsoulis, G.3
  • 6
    • 0001038826 scopus 로고
    • Covariance selection
    • A. P. Dempster. Covariance selection. Biometrics, 28(1):157-175, 1972.
    • (1972) Biometrics , vol.28 , Issue.1 , pp. 157-175
    • Dempster, A.P.1
  • 9
    • 45849134070 scopus 로고    scopus 로고
    • Sparse inverse covariance estimation with the graphical lasso
    • J. Friedman, T. Hastie, and R. Tibshirani. Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9(3):432-441, 2008.
    • (2008) Biostatistics , vol.9 , Issue.3 , pp. 432-441
    • Friedman, J.1    Hastie, T.2    Tibshirani, R.3
  • 10
    • 0000673504 scopus 로고
    • Multivariate generalizations of the Wolfowitz and Smirnov two-sample tests
    • J. H. Friedman and L. C. Rafsky. Multivariate generalizations of the Wolfowitz and Smirnov two-sample tests. Annals of Statistics, 7:697-717, 1979.
    • (1979) Annals of Statistics , vol.7 , pp. 697-717
    • Friedman, J.H.1    Rafsky, L.C.2
  • 14
    • 0000959190 scopus 로고
    • A multivariate two-sample test based on the number of nearest neighbor type coincidences
    • Z. Henze. A multivariate two-sample test based on the number of nearest neighbor type coincidences. Annals of Statistics, 16:772-783, 1988.
    • (1988) Annals of Statistics , vol.16 , pp. 772-783
    • Henze, Z.1
  • 16
    • 49749143714 scopus 로고    scopus 로고
    • Computing correlation anomaly scores using stochastic nearest neighbors
    • T. Idé, S. Papadimitriou, and M. Vlachos. Computing correlation anomaly scores using stochastic nearest neighbors. In Proc. IEEE Intl. Conf. Data Mining, pages 523-528, 2007.
    • (2007) Proc. IEEE Intl. Conf. Data Mining , pp. 523-528
    • Idé, T.1    Papadimitriou, S.2    Vlachos, M.3
  • 19
    • 29344460888 scopus 로고    scopus 로고
    • Using modified lasso regression to learn large undirected graphs in a probabilistic framework
    • F. Li and Y. Yang. Using modified lasso regression to learn large undirected graphs in a probabilistic framework. In Proc. National Conf. Artificial Intelligence, pages 801-806, 2005.
    • (2005) Proc. National Conf. Artificial Intelligence , pp. 801-806
    • Li, F.1    Yang, Y.2
  • 20
    • 33747163541 scopus 로고    scopus 로고
    • High-dimensional graphs and variable selection with the lasso
    • N. Meinshausen and P. Bühlmann. High-dimensional graphs and variable selection with the lasso. Annals of Statistics, 34(3):1436-1462,2006.
    • (2006) Annals of Statistics , vol.34 , Issue.3 , pp. 1436-1462
    • Meinshausen, N.1    Bühlmann, P.2
  • 21
    • 34249862287 scopus 로고    scopus 로고
    • Learning causal networks from systems biology time course data: An effective model selection procedure for the vector autoregressive process
    • R. Opgen-Rhein and K. Strimmer. Learning causal networks from systems biology time course data: an effective model selection procedure for the vector autoregressive process. BMC Bioinformatics, 8(Suppl.2):S3, 2007.
    • (2007) BMC Bioinformatics , vol.8 , Issue.SUPPL.2
    • Opgen-Rhein, R.1    Strimmer, K.2
  • 25
    • 34547991011 scopus 로고    scopus 로고
    • X. Xuan and K. Murphy. Modeling changing dependency structure in multivariate time series. In Proc. the 24th Intl. Conf. Machine Learning, pages 1055-1062, 2007.
    • X. Xuan and K. Murphy. Modeling changing dependency structure in multivariate time series. In Proc. the 24th Intl. Conf. Machine Learning, pages 1055-1062, 2007.
  • 26
    • 33846114377 scopus 로고    scopus 로고
    • The adaptive lasso and its oracle properties
    • H. Zou. The adaptive lasso and its oracle properties. Journal of the American Statistical Association, 101 (476): 1418-1429, 2006.
    • (2006) Journal of the American Statistical Association , vol.101 , Issue.476 , pp. 1418-1429
    • Zou, H.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.