메뉴 건너뛰기




Volumn 29, Issue 2, 2011, Pages 457-478

Convex non-negative matrix factorization for massive datasets

Author keywords

Data mining; Information retrieval; Large scale data analysis; Low rank approximation; Matrix factorization

Indexed keywords


EID: 80053924421     PISSN: 02191377     EISSN: 02193116     Source Type: Journal    
DOI: 10.1007/s10115-010-0352-6     Document Type: Article
Times cited : (37)

References (31)
  • 1
    • 68349150835 scopus 로고    scopus 로고
    • On classification and segmentation of massive audio data streams
    • Aggarwal C (2009) On classification and segmentation of massive audio data streams. Knowl Inf Syst 20(2): 137-156.
    • (2009) Knowl Inf Syst , vol.20 , Issue.2 , pp. 137-156
    • Aggarwal, C.1
  • 2
    • 0001218562 scopus 로고
    • The statistical analysis of compositional data
    • Aitchison J (1982) The statistical analysis of compositional data. J R Stat Soc B 44(2): 139-177.
    • (1982) J R Stat Soc B , vol.44 , Issue.2 , pp. 139-177
    • Aitchison, J.1
  • 4
    • 57149147265 scopus 로고    scopus 로고
    • Non-negative matrix factorization for semi-supervised data clustering
    • Chen Y, Rege M, Dong M, Hua J (2008) Non-negative matrix factorization for semi-supervised data clustering. Knowl Inf Syst 17(3): 355-379.
    • (2008) Knowl Inf Syst , vol.17 , Issue.3 , pp. 355-379
    • Chen, Y.1    Rege, M.2    Dong, M.3    Hua, J.4
  • 5
    • 0028532769 scopus 로고
    • Archetypal analysis
    • Cutler A, Breiman L (1994) Archetypal analysis. Technometrics 36(4): 338-347.
    • (1994) Technometrics , vol.36 , Issue.4 , pp. 338-347
    • Cutler, A.1    Breiman, L.2
  • 8
    • 49149085985 scopus 로고    scopus 로고
    • When does non-negative matrix factorization give a correct decomposition into parts?
    • MIT Press
    • Donoho D, Stodden V (2004) When does non-negative matrix factorization give a correct decomposition into parts?. In: Advances in neural information processing systems 16. MIT Press.
    • (2004) Advances in neural information processing systems , vol.16
    • Donoho, D.1    Stodden, V.2
  • 9
    • 33751097630 scopus 로고    scopus 로고
    • Fast Monte Carlo algorithms III: computing a compressed approixmate matrix decomposition
    • Drineas P, Kannan R, Mahoney M (2006), Fast Monte Carlo algorithms III: computing a compressed approixmate matrix decomposition. SIAM J Comput 36(1): 184-206.
    • (2006) SIAM J Comput , vol.36 , Issue.1 , pp. 184-206
    • Drineas, P.1    Kannan, R.2    Mahoney, M.3
  • 10
    • 84976803260 scopus 로고
    • FastMap: a fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets
    • Faloutsos C, Lin K-I (1995) FastMap: a fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets. In: Proceedings of ACM SIGMOD conference.
    • (1995) Proceedings of ACM SIGMOD conference
    • Faloutsos, C.1    Lin, K.-I.2
  • 12
    • 70849126253 scopus 로고    scopus 로고
    • The unreasonable effectiveness of data
    • Halevy A, Norvig P, Pereira F (2009) The unreasonable effectiveness of data. IEEE Intell Syst 24(2): 8-12.
    • (2009) IEEE Intell Syst , vol.24 , Issue.2 , pp. 8-12
    • Halevy, A.1    Norvig, P.2    Pereira, F.3
  • 13
    • 84900510076 scopus 로고    scopus 로고
    • Non-negative matrix factorization with sparseness constraints
    • Hoyer P (2004) Non-negative matrix factorization with sparseness constraints. J Mach Learn 5(Dec): 1457-1469.
    • (2004) J Mach Learn , vol.5 , Issue.Dec , pp. 1457-1469
    • Hoyer, P.1
  • 14
    • 22844457314 scopus 로고    scopus 로고
    • Limit theorems for the convex hull of random points in higher dimensions
    • Hueter I (1999) Limit theorems for the convex hull of random points in higher dimensions. Trans Am Math Soc 351(11): 4337-4363.
    • (1999) Trans Am Math Soc , vol.351 , Issue.11 , pp. 4337-4363
    • Hueter, I.1
  • 17
    • 58249092020 scopus 로고    scopus 로고
    • Non-negative matrix factorization: ill-posedness and a geometric algorithm
    • Klingenberg B, Curry J, Dougherty A (2008) Non-negative matrix factorization: ill-posedness and a geometric algorithm. Pattern Recogn 42(5): 918-928.
    • (2008) Pattern Recogn , vol.42 , Issue.5 , pp. 918-928
    • Klingenberg, B.1    Curry, J.2    Dougherty, A.3
  • 19
    • 0033592606 scopus 로고    scopus 로고
    • Learning the parts of objects by non-negative matrix factorization
    • Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755): 788-799.
    • (1999) Nature , vol.401 , Issue.6755 , pp. 788-799
    • Lee, D.D.1    Seung, H.S.2
  • 20
    • 54049130028 scopus 로고    scopus 로고
    • Clustering based on matrix approximation: a unifying view
    • Li T (2008) Clustering based on matrix approximation: a unifying view. Knowl Inf Syst 17(1): 1-15.
    • (2008) Knowl Inf Syst , vol.17 , Issue.1 , pp. 1-15
    • Li, T.1
  • 21
    • 0035328421 scopus 로고    scopus 로고
    • Modeling the shape of the scene: a holistic representation of the spatial envelope
    • Olivia A, Torralba A (2001) Modeling the shape of the scene: a holistic representation of the spatial envelope. Int J Comput Vis 42(3): 145-175.
    • (2001) Int J Comput Vis , vol.42 , Issue.3 , pp. 145-175
    • Olivia, A.1    Torralba, A.2
  • 22
    • 24344449655 scopus 로고    scopus 로고
    • On FastMap and the convex hull of multivariate data: toward fast and robust dimension reduction
    • Ostrouchov G, Samatova N (2005) On FastMap and the convex hull of multivariate data: toward fast and robust dimension reduction. IEEE Trans Pattern Anal Mach Intell 27(8): 1340-1434.
    • (2005) IEEE Trans Pattern Anal Mach Intell , vol.27 , Issue.8 , pp. 1340-1434
    • Ostrouchov, G.1    Samatova, N.2
  • 23
    • 0028561099 scopus 로고
    • Positive matrix factorization: a non-negative factor model with optimal utilization of error estimates of data values
    • Paatero P, Tapper U (1994) Positive matrix factorization: a non-negative factor model with optimal utilization of error estimates of data values. Environmetrics 5(2): 111-126.
    • (1994) Environmetrics , vol.5 , Issue.2 , pp. 111-126
    • Paatero, P.1    Tapper, U.2
  • 29
    • 54749092170 scopus 로고    scopus 로고
    • 80 Million tiny images: a large data set for nonparametric object and scene recognition
    • Torralba A, Fergus R, Freeman WT (2008) 80 Million tiny images: a large data set for nonparametric object and scene recognition. IEEE Trans Pattern Anal Mach Intell 30(11): 1958-1970.
    • (2008) IEEE Trans Pattern Anal Mach Intell , vol.30 , Issue.11 , pp. 1958-1970
    • Torralba, A.1    Fergus, R.2    Freeman, W.T.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.