메뉴 건너뛰기




Volumn 26, Issue 6, 2012, Pages 290-298

Robust PARAFAC for incomplete data

Author keywords

Expectation maximization; Incomplete data; Multiway; Principal component analysis; Robustness

Indexed keywords

LEAST SQUARES APPROXIMATIONS; MAXIMUM PRINCIPLE; ROBUSTNESS (CONTROL SYSTEMS);

EID: 84862771000     PISSN: 08869383     EISSN: 1099128X     Source Type: Journal    
DOI: 10.1002/cem.2452     Document Type: Article
Times cited : (18)

References (20)
  • 1
    • 0030813663 scopus 로고    scopus 로고
    • PARAFAC. Tutorial and applications
    • Bro R. PARAFAC. Tutorial and applications. Chemom. Intell. Lab. Syst. 1997; 38: 149-171.
    • (1997) Chemom. Intell. Lab. Syst. , vol.38 , pp. 149-171
    • Bro, R.1
  • 2
    • 0036831022 scopus 로고    scopus 로고
    • High breakdown estimation of multivariate location and scale with missing observations
    • Cheng T-C, Victoria-Feser M. High breakdown estimation of multivariate location and scale with missing observations. Br. J. Math. Stat. Psychol. 2002; 55: 317-335.
    • (2002) Br. J. Math. Stat. Psychol. , vol.55 , pp. 317-335
    • Cheng, T.-C.1    Victoria-Feser, M.2
  • 4
    • 80053385926 scopus 로고    scopus 로고
    • Detecting outlying samples in a parallel factor analysis model
    • Engelen S, Hubert M. Detecting outlying samples in a parallel factor analysis model. Analytica Chemica Acta 2011; 705: 155-165.
    • (2011) Analytica Chemica Acta , vol.705 , pp. 155-165
    • Engelen, S.1    Hubert, M.2
  • 5
    • 33847284508 scopus 로고    scopus 로고
    • Automatically identifying scatter in fluorescence data using robust techniques
    • Engelen S, Frosch Møller S, Hubert M. Automatically identifying scatter in fluorescence data using robust techniques. Chemom. Intell. Lab. Syst. 2007; 86: 35-51.
    • (2007) Chemom. Intell. Lab. Syst. , vol.86 , pp. 35-51
    • Engelen, S.1    Frosch Møller, S.2    Hubert, M.3
  • 6
    • 64249141028 scopus 로고    scopus 로고
    • A fully robust PARAFAC method for analyzing fluorescence data
    • Engelen S, Frosch Møller S, Jorgensen BM. A fully robust PARAFAC method for analyzing fluorescence data. J. Chemom. 2009; 23: 124-131.
    • (2009) J. Chemom. , vol.23 , pp. 124-131
    • Engelen, S.1    Frosch Møller, S.2    Jorgensen, B.M.3
  • 7
    • 34247395413 scopus 로고    scopus 로고
    • Fast cross-validation for high-breakdown resampling algorithms for PCA
    • Hubert M, Engelen S. Fast cross-validation for high-breakdown resampling algorithms for PCA. Computational Statistics and Data Analysis 2007; 51: 5013-5024.
    • (2007) Computational Statistics and Data Analysis , vol.51 , pp. 5013-5024
    • Hubert, M.1    Engelen, S.2
  • 8
    • 13444287831 scopus 로고    scopus 로고
    • ROBPCA: a new approach to robust principal components analysis
    • Hubert M, Rousseeuw PJ, Vanden Branden K. ROBPCA: a new approach to robust principal components analysis. Technometrics 2005; 47: 64-79.
    • (2005) Technometrics , vol.47 , pp. 64-79
    • Hubert, M.1    Rousseeuw, P.J.2    Vanden Branden, K.3
  • 9
    • 49749129645 scopus 로고    scopus 로고
    • High breakdown robust multivariate methods
    • Hubert M, Rousseeuw PJ, Van Aelst S. High breakdown robust multivariate methods. Stat. Sci. 2008; 23: 92-119.
    • (2008) Stat. Sci. , vol.23 , pp. 92-119
    • Hubert, M.1    Rousseeuw, P.J.2    Van Aelst, S.3
  • 11
    • 0023842809 scopus 로고
    • Robust estimation of the mean and covariance matrix from data with missing values
    • Little RJA. Robust estimation of the mean and covariance matrix from data with missing values. Applied Statistics 1988; 37: 23-38.
    • (1988) Applied Statistics , vol.37 , pp. 23-38
    • Little, R.J.A.1
  • 13
    • 84950968334 scopus 로고
    • Least median of squares regression
    • Rousseeuw PJ. Least median of squares regression. J. Am. Stat. Assoc. 1984; 79: 871-880.
    • (1984) J. Am. Stat. Assoc. , vol.79 , pp. 871-880
    • Rousseeuw, P.J.1
  • 14
    • 35548956430 scopus 로고    scopus 로고
    • Principal component analysis for data containing outliers and missing elements
    • Serneels S, Verdonck T. Principal component analysis for data containing outliers and missing elements. Computational Statistics and Data Analysis 2008; 52: 1712-1727.
    • (2008) Computational Statistics and Data Analysis , vol.52 , pp. 1712-1727
    • Serneels, S.1    Verdonck, T.2
  • 15
    • 65749090221 scopus 로고    scopus 로고
    • Principal component regression for data containing outliers and missing elements
    • Serneels S, Verdonck T. Principal component regression for data containing outliers and missing elements. Computational Statistics and Data Analysis 2009; 53: 3855-3863.
    • (2009) Computational Statistics and Data Analysis , vol.53 , pp. 3855-3863
    • Serneels, S.1    Verdonck, T.2
  • 17
    • 33751549943 scopus 로고    scopus 로고
    • How to construct a multiple regression model for data with missing elements and outlying objects
    • Stanimirova I, Serneels S, Van Espen P, Walczak B. How to construct a multiple regression model for data with missing elements and outlying objects. Anal. Chim. Acta 2007; 581(2): 324-332.
    • (2007) Anal. Chim. Acta , vol.581 , Issue.2 , pp. 324-332
    • Stanimirova, I.1    Serneels, S.2    Van Espen, P.3    Walczak, B.4
  • 19
    • 30144444694 scopus 로고    scopus 로고
    • A comparison of algorithms for fitting the PARAFAC model
    • Tomasi G, Bro R. A comparison of algorithms for fitting the PARAFAC model. Computational Statistics and Data analysis 2006; 50: 1700-1734.
    • (2006) Computational Statistics and Data analysis , vol.50 , pp. 1700-1734
    • Tomasi, G.1    Bro, R.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.