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Volumn , Issue , 2006, Pages 265-270

Sanger-driven MDSLocalize - A comparative study for Genomic Data

Author keywords

Dimension reduction; MDS; Proximity data; Visualization

Indexed keywords

CLUSTERING ALGORITHMS; DNA SEQUENCES; FLOW VISUALIZATION; GENE EXPRESSION; MENDELEVIUM; NEURAL NETWORKS; SINGULAR VALUE DECOMPOSITION; VISUALIZATION;

EID: 34250302879     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (4)

References (11)
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    • Distance matrix reconstruction from incomplete distance information for sensor network localization
    • 110402, ENALAB, Yale University
    • P. Drineas, M. Magdon-Ismail, G. Pandurangan, R. Virrankoski, and A. Savvides. Distance matrix reconstruction from incomplete distance information for sensor network localization. Technical report, 110402, ENALAB, Yale University, 2005.
    • (2005) Technical Report
    • Drineas, P.1    Magdon-Ismail, M.2    Pandurangan, G.3    Virrankoski, R.4    Savvides, A.5
  • 4
    • 0000071395 scopus 로고
    • Some distance properties of latent root and vector methods used in multivariate analysis
    • J. Gower. Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika, 53:325-338, 1966.
    • (1966) Biometrika , vol.53 , pp. 325-338
    • Gower, J.1
  • 5
    • 0032602777 scopus 로고    scopus 로고
    • A stochastic self-organizing map for proximity data
    • T. Graepel and K. Obermayer. A stochastic self-organizing map for proximity data. Neural Computation, 11(1):139-155, 1999.
    • (1999) Neural Computation , vol.11 , Issue.1 , pp. 139-155
    • Graepel, T.1    Obermayer, K.2
  • 6
    • 0036790769 scopus 로고    scopus 로고
    • How to make large self-organizing maps for nonvectorial data
    • T. Kohonen and P. Somervuo. How to make large self-organizing maps for nonvectorial data. Neural Networks, 15(8-9):945-952, 2002.
    • (2002) Neural Networks , vol.15 , Issue.8-9 , pp. 945-952
    • Kohonen, T.1    Somervuo, P.2
  • 7
    • 0004249746 scopus 로고    scopus 로고
    • editors. Elsevier, Amsterdam, 1st edition
    • E. Oja and S. Kaski, editors. Kohonen Maps. Elsevier, Amsterdam, 1st edition, 1999.
    • (1999) Kohonen Maps
    • Oja, E.1    Kaski, S.2
  • 8
    • 31144457383 scopus 로고    scopus 로고
    • Self-organizing map-based discovery and visualization of human endogenous retroviral sequence groups
    • M. Oja, G. Sperper, J. Blomberg, and S. Kaski. Self-organizing map-based discovery and visualization of human endogenous retroviral sequence groups. International Journal of Neural Systems, 15(3):163-179, 2005.
    • (2005) International Journal of Neural Systems , vol.15 , Issue.3 , pp. 163-179
    • Oja, M.1    Sperper, G.2    Blomberg, J.3    Kaski, S.4
  • 9
    • 0024883243 scopus 로고
    • Optimal unsupervised learning in a single-layer network
    • T. Sanger. Optimal unsupervised learning in a single-layer network. Neur. Netw., 2:459-473, 1989.
    • (1989) Neur. Netw. , vol.2 , pp. 459-473
    • Sanger, T.1
  • 11
    • 84958976659 scopus 로고    scopus 로고
    • Neighborhood preservation in nonlinear projection methods: An experimental study
    • G. Dorffner, H. Bischof, and K. Hornik, editors, Springer
    • J. Venna and S. Kaski. Neighborhood preservation in nonlinear projection methods: An experimental study. In G. Dorffner, H. Bischof, and K. Hornik, editors, Proceedings of the International Conference on Artificial Neural Networks (ICANN), pages 485-591. Springer, 2001.
    • (2001) Proceedings of the International Conference on Artificial Neural Networks (ICANN) , pp. 485-591
    • Venna, J.1    Kaski, S.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.