메뉴 건너뛰기




Volumn 61, Issue 22, 2013, Pages 5620-5632

Sparse and non-negative BSS for noisy data

Author keywords

BSS; Morphological diversity; NMF; Sparsity

Indexed keywords

MORPHOLOGICAL COMPONENT ANALYSIS; MORPHOLOGICAL DIVERSITY; NMF; NON-NEGATIVITY CONSTRAINTS; NONNEGATIVE MATRIX FACTORIZATION; NUMERICAL EXPERIMENTS; SPARSITY; STATE-OF-THE-ART ALGORITHMS;

EID: 84886683229     PISSN: 1053587X     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSP.2013.2279358     Document Type: Article
Times cited : (34)

References (39)
  • 1
    • 31044445386 scopus 로고    scopus 로고
    • Email surveillance using non-negative matrix factorization
    • DOI 10.1007/s10588-005-5380-5
    • M. W. Berry and M. Browne, "Email surveillance using non-negative matrix factorization," Comput. Math. Organizat. Theory, vol. 11, no. 3, pp. 249-264, 2005. (Pubitemid 43118969)
    • (2005) Computational and Mathematical Organization Theory , vol.11 , Issue.3 , pp. 249-264
    • Berry, M.W.1    Browne, M.2
  • 3
    • 63249085556 scopus 로고    scopus 로고
    • Nonnegative matrix factorization with the Itakura-Saito divergence: With application to music analysis
    • C. Févotte, N. Bertin, and J.-L. Durrieu, "Nonnegative matrix factorization with the Itakura-Saito divergence: With application to music analysis," Neural Comput., vol. 21, no. 3, pp. 793-830, 2009.
    • (2009) Neural Comput. , vol.21 , Issue.3 , pp. 793-830
    • Févotte, C.1    Bertin, N.2    Durrieu, J.-L.3
  • 4
    • 84869815904 scopus 로고    scopus 로고
    • Weighted NMF for highresolution mass spectrometry analysis
    • R. Dubroca, C. Junot, and A. Souloumiac, "Weighted NMF for highresolution mass spectrometry analysis," in Proc. EUSIPCO, 2012, pp. 1806-1810.
    • (2012) Proc. EUSIPCO , pp. 1806-1810
    • Dubroca, R.1    Junot, C.2    Souloumiac, A.3
  • 5
    • 0033592606 scopus 로고    scopus 로고
    • Learning the parts of objects by non-negative matrix factorization
    • D. D. Lee and H. S. Seung, "Learning the parts of objects by non-negative matrix factorization," Nature, vol. 401, no. 6755, pp. 788-791, 1999.
    • (1999) Nature , vol.401 , Issue.6755 , pp. 788-791
    • Lee, D.D.1    Seung, H.S.2
  • 7
    • 0028561099 scopus 로고
    • Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values
    • P. Paatero and U. Tapper, "Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values," Environmetrics, vol. 5, no. 2, pp. 111-126, 1994.
    • (1994) Environmetrics , vol.5 , Issue.2 , pp. 111-126
    • Paatero, P.1    Tapper, U.2
  • 8
    • 73249153369 scopus 로고    scopus 로고
    • On the complexity of nonnegative matrix factorization
    • S. A. Vavasis, "On the complexity of nonnegative matrix factorization," SIAM J. Optim., vol. 20, no. 3, pp. 1364-1377, 2009.
    • (2009) SIAM J. Optim. , vol.20 , Issue.3 , pp. 1364-1377
    • Vavasis, S.A.1
  • 9
    • 84886693356 scopus 로고    scopus 로고
    • Robust non-negative matrix factorization for multispectral data with sparse prior
    • Cargese, France, May 14-18
    • J. Rapin, J. Bobin, A. Larue, and J.-L. Starck, "Robust non-negative matrix factorization for multispectral data with sparse prior," in Proc. ADA7, Cargese, France, May 14-18, 2012.
    • (2012) Proc. ADA7
    • Rapin, J.1    Bobin, J.2    Larue, A.3    Starck, J.-L.4
  • 10
    • 84898964201 scopus 로고    scopus 로고
    • Algorithms for non-negative matrix factorization
    • D. D. Lee and H. S. Seung, "Algorithms for non-negative matrix factorization," Adv. Neural Inf. Process. Syst., vol. 13, no. 1, pp. 556-562, 2001.
    • (2001) Adv. Neural Inf. Process. Syst. , vol.13 , Issue.1 , pp. 556-562
    • Lee, D.D.1    Seung, H.S.2
  • 12
    • 34547198396 scopus 로고    scopus 로고
    • Algorithms and applications for approximate nonnegative matrix factorization
    • DOI 10.1016/j.csda.2006.11.006, PII S0167947306004191
    • M. Berry, M. Browne, A. Langville, V. Pauca, and R. Plemmons, "Algorithms and applications for approximate nonnegative matrix factorization," Comput. Statist. Data Anal., vol. 52, no. 1, pp. 155-173, 2007. (Pubitemid 47331703)
    • (2007) Computational Statistics and Data Analysis , vol.52 , Issue.1 , pp. 155-173
    • Berry, M.W.1    Browne, M.2    Langville, A.N.3    Pauca, V.P.4    Plemmons, R.J.5
  • 14
    • 23744469721 scopus 로고    scopus 로고
    • Interior-point gradient method for large-scale totally nonnegative least squares problems
    • DOI 10.1007/s10957-005-2668-z
    • M. Merritt and Y. Zhang, "Interior-point gradient method for largescale totally nonnegative least squares problems," J. Optim. Theory Appl., vol. 126, no. 1, pp. 191-202, 2005. (Pubitemid 41125942)
    • (2005) Journal of Optimization Theory and Applications , vol.126 , Issue.1 , pp. 191-202
    • Merritt, M.1    Zhang, Y.2
  • 15
    • 34247173538 scopus 로고    scopus 로고
    • Nonnegative matrix factorization with constrained second-order optimization
    • DOI 10.1016/j.sigpro.2007.01.024, PII S0165168407000527
    • R. Zdunek and A. Cichocki, "Nonnegative matrix factorization with constrained second-order optimization," Signal Process., vol. 87, no. 8, pp. 1904-1916, 2007. (Pubitemid 46590394)
    • (2007) Signal Processing , vol.87 , Issue.8 , pp. 1904-1916
    • Zdunek, R.1    Cichocki, A.2
  • 16
    • 37749030729 scopus 로고    scopus 로고
    • Hierarchical ALS algorithms for nonnegative matrix and 3D tensor factorization
    • A. Cichocki, R. Zdunek, and S.-I. Amari, "Hierarchical ALS algorithms for nonnegative matrix and 3D tensor factorization," in Proc. ICA, 2007, pp. 169-176.
    • (2007) Proc. ICA , pp. 169-176
    • Cichocki, A.1    Zdunek, R.2    Amari, S.-I.3
  • 18
    • 35548969471 scopus 로고    scopus 로고
    • Projected gradient methods for nonnegative matrix factorization
    • C.-J. Lin, "Projected gradient methods for nonnegative matrix factorization," Neural Comput., vol. 19, no. 10, pp. 2756-2779, 2007.
    • (2007) Neural Comput. , vol.19 , Issue.10 , pp. 2756-2779
    • Lin, C.-J.1
  • 19
    • 84861164231 scopus 로고    scopus 로고
    • NeNMF: An optimal gradientmethod for nonnegativematrix factorization
    • N. Guan, D. Tao, Z. Luo, and B. Yuan, "NeNMF: An optimal gradientmethod for nonnegativematrix factorization," IEEE Trans. Signal Process., vol. 60, no. 6, pp. 2882-2898, 2012.
    • (2012) IEEE Trans. Signal Process. , vol.60 , Issue.6 , pp. 2882-2898
    • Guan, N.1    Tao, D.2    Luo, Z.3    Yuan, B.4
  • 21
    • 84857265500 scopus 로고    scopus 로고
    • Geometrical method using simplicial cones for overdetermined nonnegative blind source separation: Application to real PET images
    • W. S. Ouedraogo, A. Souloumiac, M. Jaïdane, and C. Jutten, "Geometrical method using simplicial cones for overdetermined nonnegative blind source separation: Application to real PET images," in Proc. LVA/ICA, 2012, pp. 494-501.
    • (2012) Proc. LVA/ICA , pp. 494-501
    • Ouedraogo, W.S.1    Souloumiac, A.2    Jaïdane, M.3    Jutten, C.4
  • 23
    • 1242331294 scopus 로고    scopus 로고
    • A nonnegative PCA algorithm for independent component analysis
    • M. D. Plumbley and E. Oja, "A nonnegative PCA algorithm for independent component analysis," IEEE Trans. Neural Netw., vol. 15, no. 1, pp. 66-76, 2004.
    • (2004) IEEE Trans. Neural Netw. , vol.15 , Issue.1 , pp. 66-76
    • Plumbley, M.D.1    Oja, E.2
  • 25
    • 0000660321 scopus 로고    scopus 로고
    • Blind source separation by sparse decomposition in a signal dictionary
    • DOI 10.1162/089976601300014385
    • M. Zibulevsky and B. A. Pearlmutter, "Blind source separation by sparse decomposition in a signal dictionary," Neural Comput., vol. 13, no. 4, pp. 863-882, 2001. (Pubitemid 33594308)
    • (2001) Neural Computation , vol.13 , Issue.4 , pp. 863-882
    • Zibulevsky, M.1    Pearlmutter, B.A.2
  • 26
    • 85098781779 scopus 로고    scopus 로고
    • Sparse representation and its applications in blind source separation
    • Vancouver, BC, Canada, Dec. 8-13
    • Y. Li, A. Cichocki, S.-I. Amari, S. Shishkin, J. Cao, and F. Gu, "Sparse representation and its applications in blind source separation," in Proc. NIPS, Vancouver, BC, Canada, Dec. 8-13, 2003.
    • (2003) Proc. NIPS
    • Li, Y.1    Cichocki, A.2    Amari, S.-I.3    Shishkin, S.4    Cao, J.5    Gu, F.6
  • 27
    • 36348936685 scopus 로고    scopus 로고
    • Sparsity and morphological diversity in blind source separation
    • DOI 10.1109/TIP.2007.906256
    • J. Bobin, J.-L. Starck, J. Fadili, and Y. Moudden, "Sparsity and morphological diversity in blind source separation," IEEE Trans. Image Process., vol. 16, no. 11, pp. 2662-2674, 2007. (Pubitemid 350147648)
    • (2007) IEEE Transactions on Image Processing , vol.16 , Issue.11 , pp. 2662-2674
    • Bobin, J.1    Starck, J.-L.2    Fadili, J.3    Moudden, Y.4
  • 29
    • 84857841726 scopus 로고    scopus 로고
    • Non-negative sparse coding
    • P. O. Hoyer, "Non-negative sparse coding," in Proc. NNSP, 2002, pp. 557-565.
    • (2002) Proc. NNSP , pp. 557-565
    • Hoyer, P.O.1
  • 30
    • 84900510076 scopus 로고    scopus 로고
    • Non-negative matrix factorization with sparseness constraints
    • P. O. Hoyer, "Non-negative matrix factorization with sparseness constraints," J. Mach. Learn. Res., vol. 5, no. 5, pp. 1457-1469, 2004.
    • (2004) J. Mach. Learn. Res. , vol.5 , Issue.5 , pp. 1457-1469
    • Hoyer, P.O.1
  • 31
    • 84861111031 scopus 로고    scopus 로고
    • Accelerated multiplicative updates and hierarchical ALS algorithms for nonnegative matrix factorization
    • N. Gillis and F. Glineur, "Accelerated multiplicative updates and hierarchical ALS algorithms for nonnegative matrix factorization," Neural Comput., vol. 24, no. 4, pp. 1085-1105, 2012.
    • (2012) Neural Comput. , vol.24 , Issue.4 , pp. 1085-1105
    • Gillis, N.1    Glineur, F.2
  • 32
    • 84870868704 scopus 로고    scopus 로고
    • Sparse and unique nonnegative matrix factorization through data preprocessing
    • N. Gillis, "Sparse and unique nonnegative matrix factorization through data preprocessing," J. Mach. Learn. Res., vol. 13, pp. 3349-3386, 2012.
    • (2012) J. Mach. Learn. Res. , vol.13 , pp. 3349-3386
    • Gillis, N.1
  • 34
    • 38049150410 scopus 로고    scopus 로고
    • Regularized alternating least squares algorithms for non-negative matrix/tensor factorization
    • A. Cichocki and R. Zdunek, "Regularized alternating least squares algorithms for non-negative matrix/tensor factorization," in Proc. ISNN, 2007, pp. 793-802.
    • (2007) Proc. ISNN , pp. 793-802
    • Cichocki, A.1    Zdunek, R.2
  • 35
    • 30844438177 scopus 로고    scopus 로고
    • Signal recovery by proximal forward-backward splitting
    • DOI 10.1137/050626090
    • P. L. Combettes and V. R. Wajs, "Signal recovery by proximal forward- backward splitting," Multiscale Model. Simulat., vol. 4, no. 4, pp. 1168-1200, 2005. (Pubitemid 44932287)
    • (2005) Multiscale Modeling and Simulation , vol.4 , Issue.4 , pp. 1168-1200
    • Combettes, P.L.1    Wajs, V.R.2
  • 36
    • 84879800501 scopus 로고    scopus 로고
    • Gradient methods for minimizing composite objective function
    • Y. Nesterov, "Gradient methods for minimizing composite objective function," Math. Programm., vol. 140, no. 1, pp. 125-161, 2013.
    • (2013) Math. Programm. , vol.140 , Issue.1 , pp. 125-161
    • Nesterov, Y.1
  • 37
    • 85014561619 scopus 로고    scopus 로고
    • A fast iterative shrinkage-thresholding algorithm for linear inverse problems
    • A. Beck and M. Teboulle, "A fast iterative shrinkage-thresholding algorithm for linear inverse problems," SIAM J. Imag. Sci., vol. 2, no. 1, p. 183, 2009.
    • (2009) SIAM J. Imag. Sci. , vol.2 , Issue.1 , pp. 183
    • Beck, A.1    Teboulle, M.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.