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Volumn 23, Issue 3, 2012, Pages 315-328

Bregman iteration algorithm for sparse nonnegative matrix factorizations via alternating l1-norm minimization

Author keywords

Bregman iteration; Nonnegative matrix factorization; Sparseness; Wavelet transform

Indexed keywords

BASIS MATRIX; BREGMAN ITERATION; COEFFICIENT MATRIX; DETECTION ACCURACY; DIMENSION REDUCTION METHOD; L1 NORM; MINIMIZATION PROBLEMS; NON-NEGATIVITY; NONNEGATIVE MATRIX FACTORIZATION; SPARSE NON-NEGATIVE MATRIX FACTORIZATIONS; SPARSENESS;

EID: 84861880130     PISSN: 09236082     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11045-011-0147-2     Document Type: Article
Times cited : (11)

References (27)
  • 1
    • 67349244805 scopus 로고    scopus 로고
    • Linearized bregman iterations for compressed sensing
    • Cai, J.-F., Osher, S., Shen, Z. (2009a). Linearized Bregman iterations for compressed sensing. Mathematics of Computation, 78, 1515-1536.
    • (2009) Mathematics of Computation , vol.78 , pp. 1515-1536
    • Cai, J.-F.1    Osher, S.2    Shen, Z.3
  • 2
    • 78049353841 scopus 로고    scopus 로고
    • Linearized bregman iterations for frame-based image deblurring
    • Cai, J.-F., Osher, S., & Shen, Z. (2009b). Linearized Bregman iterations for frame-based image deblurring. SIAM Journal on Imaging Sciences, 2(1), 226-252.
    • (2009) SIAM Journal on Imaging Sciences , vol.2 , Issue.1 , pp. 226-252
    • Cai, J.-F.1    Osher, S.2    Shen, Z.3
  • 3
    • 33745604236 scopus 로고    scopus 로고
    • Stable signal recovery from incomplete and inaccurate measurements
    • DOI 10.1002/cpa.20124
    • Candès, E., Romberg, J., & Tao, T. (2006). Stable signal recovery from incomplete and inaccurate measurements. Communications Pure and Applied Mathematics, 59(8), 1207-1223. (Pubitemid 43988295)
    • (2006) Communications on Pure and Applied Mathematics , vol.59 , Issue.8 , pp. 1207-1223
    • Candes, E.J.1    Romberg, J.K.2    Tao, T.3
  • 4
    • 37749030729 scopus 로고    scopus 로고
    • Hierarchical als algorithms for nonnegative matrix and 3d tensor factorization
    • Cichocki, A., Zdunek, R., & Amari, S. I. (2007). Hierarchical ALS algorithms for nonnegative matrix and 3D tensor factorization. Springer LNCS, 4666, 169-176.
    • (2007) Springer LNCS , vol.4666 , pp. 169-176
    • Cichocki, A.1    Zdunek, R.2    Amari, S.I.3
  • 6
    • 0028416938 scopus 로고
    • Indenpendent component analysis-A new concept?
    • Comon, P. (1994). Indenpendent component analysis-A new concept?. Signal Processing, 36, 287-314.
    • (1994) Signal Processing , vol.36 , pp. 287-314
    • Comon, P.1
  • 7
    • 33646365077 scopus 로고    scopus 로고
    • 1-norm solution is also the sparsest solution
    • DOI 10.1002/cpa.20132
    • Donoho, D. (2006). For most large underdetermined systems of linear equations the minimal l1-norm solution is also the sparsest Solution. Communications Pure and Applied Mathematics, 59(6), 797-829. (Pubitemid 43667226)
    • (2006) Communications on Pure and Applied Mathematics , vol.59 , Issue.6 , pp. 797-829
    • Donoho, D.L.1
  • 10
    • 0041339710 scopus 로고    scopus 로고
    • Introducing a weighted non-negative matrix factorization for image classification
    • DOI 10.1016/S0167-8655(03)00089-8
    • Guillamet, D., Vitria, J., & Schiele, B. (2003). Introducing a weighted non-negative matrix factorization for image classification. Pattern Recognition Letters, 24, 2447-2454. (Pubitemid 36911088)
    • (2003) Pattern Recognition Letters , vol.24 , Issue.14 , pp. 2447-2454
    • Guillamet, D.1    Vitria, J.2    Schiele, B.3
  • 11
    • 84857841726 scopus 로고    scopus 로고
    • Non-negative sparse coding
    • (Proceedings of IEEE workshop on neural networks for signal processing) (pp. 557-565). Martigny, Switzerland
    • Hoyer, P. O. (2002). Non-negative sparse coding. In Neural networks for signal processing XII (Proceedings of IEEE workshop on neural networks for signal processing) (pp. 557-565). Martigny, Switzerland.
    • (2002) Neural networks for signal processing , vol.12
    • Hoyer, P.O.1
  • 12
    • 84900510076 scopus 로고    scopus 로고
    • Nonnegative matrix factorization with sparseness constraints
    • Hoyer, P. O. (2004). Nonnegative matrix factorization with sparseness constraints. Journal of Machine Learning Research, 5, 1457-1469.
    • (2004) Journal of Machine Learning Research , vol.5 , pp. 1457-1469
    • Hoyer, P.O.1
  • 13
    • 34547844077 scopus 로고    scopus 로고
    • Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis
    • DOI 10.1093/bioinformatics/btm134
    • Kim H., & Park H. (2007). Sparse non-negative matrix factorizations via alternating non-negativityconstrained least squares for microarray data analysis. Bioinformatics, 23(12), 1495-1502. (Pubitemid 47244474)
    • (2007) Bioinformatics , vol.23 , Issue.12 , pp. 1495-1502
    • Kim, H.1    Park, H.2
  • 14
    • 0033592606 scopus 로고    scopus 로고
    • Learning the parts of objects by non-negative matrix factorization
    • Lee, D. D., & Seung H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788-791.
    • (1999) Nature , vol.401 , Issue.6755 , pp. 788-791
    • Lee, D.D.1    Seung, H.S.2
  • 17
    • 22944481836 scopus 로고    scopus 로고
    • Face recognition using wavelet transform and non-negative matrix factorization
    • Berlin: Springer
    • Neo H. F., Teoh, B. J., & Ngo, C. L. (2004). Face recognition using wavelet transform and non-negative matrix factorization. Lecture note on AI, vol. 3339 (pp. 192-202), Berlin: Springer.
    • (2004) Lecture note on AI , vol.3339 , pp. 192-202
    • Neo, H.F.1    Teoh, B.J.2    Ngo, C.L.3
  • 18
    • 19844370110 scopus 로고    scopus 로고
    • An iterative regularization method for total variation-based image restoration
    • DOI 10.1137/040605412
    • Osher, S., Burger, M., Goldfarb, D., Xu, J., & Yin, W. (2005). An iterative regularization method for total variation-based image restoration. Multiscale Model and Simulation, 4, 460-489. (Pubitemid 43885248)
    • (2005) Multiscale Modeling and Simulation , vol.4 , Issue.2 , pp. 460-489
    • Osher, S.1    Burger, M.2    Goldfarb, D.3    Xu, J.4    Yin, W.5
  • 21
    • 33646682646 scopus 로고    scopus 로고
    • Nonnegative matrix factorization for spectral data analysis
    • DOI 10.1016/j.laa.2005.06.025, PII S002437950500340X
    • Pauca, V. P., Piper, J., & Plemmons, R. J. (2006). Nonnegative matrix factorization for spectral data analysis. Linear Algebra and Applications, 416(1), 29-47. (Pubitemid 43737212)
    • (2006) Linear Algebra and Its Applications , vol.416 , Issue.1 , pp. 29-47
    • Pauca, V.P.1    Piper, J.2    Plemmons, R.J.3
  • 23
    • 0000209334 scopus 로고    scopus 로고
    • Local feature analysis: A general statistical theory for object representation
    • Penev, P., & Atick, J. (1996). Local feature analysis: A general statistical theory for object representation. Neural Systems, 7(3), 477-500. (Pubitemid 126717004)
    • (1996) Network: Computation in Neural Systems , vol.7 , Issue.3 , pp. 477-500
    • Penev, P.S.1    Atick, J.J.2
  • 27
    • 84977895355 scopus 로고    scopus 로고
    • Bregman iterative algorithms for l1-minimization with applications to compressed sensing
    • Yin, W., Osher, S., Goldfarb, D., & Darbon, J. (2008). Bregman iterative algorithms for l1-minimization with applications to compressed sensing. SIAM Journal on Imaging Sciences, 1, 143-168.
    • (2008) SIAM Journal on Imaging Sciences , vol.1 , pp. 143-168
    • Yin, W.1    Osher, S.2    Goldfarb, D.3    Darbon, J.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.