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Volumn 82, Issue 3, 2017, Pages 778-794

Sparse Exploratory Factor Analysis

Author keywords

eigenvalue reparameterization; optimization on matrix manifolds; penalties inducing sparseness

Indexed keywords


EID: 85023753659     PISSN: 00333123     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11336-017-9575-8     Document Type: Article
Times cited : (27)

References (21)
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  • 4
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    • Runge–Kutta type methods based on geodesics for systems of ODEs on the Stiefel manifold
    • Del Buono, N., & Lopez, L. (2001). Runge–Kutta type methods based on geodesics for systems of ODEs on the Stiefel manifold. BIT Numerical Mathematics, 41(5), 912–923.
    • (2001) BIT Numerical Mathematics , vol.41 , Issue.5 , pp. 912-923
    • Del Buono, N.1    Lopez, L.2
  • 8
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    • Harman, H.H.1
  • 9
    • 84902142486 scopus 로고    scopus 로고
    • Estimation of an oblique structure via penalized likelihood factor analysis
    • Hirose, K., & Yamamoto, M. (2014). Estimation of an oblique structure via penalized likelihood factor analysis. Computational Statistics and Data Analysis, 79, 120–132.
    • (2014) Computational Statistics and Data Analysis , vol.79 , pp. 120-132
    • Hirose, K.1    Yamamoto, M.2
  • 10
    • 84938416978 scopus 로고    scopus 로고
    • Sparse estimation via nonconcave penalized likelihood in a factor analysis model
    • Hirose, K., & Yamamoto, M. (2015). Sparse estimation via nonconcave penalized likelihood in a factor analysis model. Statistics and Computing, 25, 863–875.
    • (2015) Statistics and Computing , vol.25 , pp. 863-875
    • Hirose, K.1    Yamamoto, M.2
  • 12
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    • Jöreskog, K. G. (1977). Factor analysis by least-squares and maximum likelihood methods. In K. Enslein, A. Ralston, & H. S. Wilf (Eds.), Mathematical methods for digital computers (pp. 125–153). New York, NY: John Wiley & Sons.
    • (1977) Mathematical methods for digital computers , pp. 125-153
    • Jöreskog, K.G.1
  • 13
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    • Conditional gradient algorithms for rank-one matrix approximations with a sparsity constraint
    • Luss, R., & Teboulle, M. (2013). Conditional gradient algorithms for rank-one matrix approximations with a sparsity constraint. SIAM Review, 55, 65–98.
    • (2013) SIAM Review , vol.55 , pp. 65-98
    • Luss, R.1    Teboulle, M.2
  • 14
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    • The MathWorks Inc, New York, NY
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    • From simple structure to sparse components: A review
    • Trendafilov, N. T. (2014). From simple structure to sparse components: A review. Computational Statistics, 29, 431–454.
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    • Trendafilov, N.T.1
  • 19
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    • Sparse versus simple structure loadings
    • PID: 25080868
    • Trendafilov, N. T., & Adachi, K. (2015). Sparse versus simple structure loadings. Psychometrika, 80, 776–790.
    • (2015) Psychometrika , vol.80 , pp. 776-790
    • Trendafilov, N.T.1    Adachi, K.2
  • 21
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    • A feasible method for optimization with orthogonality constraints
    • Wen, Z., & Yin, W. (2013). A feasible method for optimization with orthogonality constraints. Mathematical Programming, 142, 397–434.
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    • Wen, Z.1    Yin, W.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.