A new linear initialization in SOM for biomolecular data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5488 LNBI, Issue , 2009, Pages 177-187

  Fiannaca, Antonino ^a,c   Di Fatta, Giuseppe ^b   Urso, Alfonso ^a   Rizzo, Riccardo ^a   Gaglio, Salvatore ^a,c  

^a CNR   (Italy)

^b UNIVERSITY OF READING   (United Kingdom)

^c UNIVERSITY OF PALERMO   (Italy)

Author keywords

Linear Initialization; Self Organizing Maps; Unsupervised Learning

Indexed keywords

BIO-MOLECULAR; BIOLOGICAL DATA; BIOLOGICAL FIELDS; DATA INPUT; EXPERIMENTAL TEST; INITIALIZATION TECHNIQUE; LEARNING SPEED; LINEAR INITIALIZATION; NEW TOOLS; QUALITY OF RESULTS; QUANTIZATION ERRORS; UNDIRECTED GRAPH; UNSUPERVISED NEURAL NETWORKS;

ARTIFICIAL INTELLIGENCE; BIOINFORMATICS; COMPUTATIONAL METHODS; EDUCATION; LEARNING ALGORITHMS; STRENGTH OF MATERIALS; UNSUPERVISED LEARNING;

SELF ORGANIZING MAPS;

EID: 70349322628     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-02504-4_16     Document Type: Conference Paper

References (17)

1. Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer, Berlin (2001)
2. Varsta, M.: Self organizing maps in sequence processing. Dissertation, Department of Electrical and Communications Engineering, Helsinki University of Technology (2002)
3. Vesanto, J., Alhoniemi, E.: Clustering of the Self-Organizing Map. J. IEEENN 11(3), 586-600 (2000)
4. Zupan, J., Gasteiger, J.: Neural Networks for Chemists: An Introduction, pp. 121-122. VCH, Weinheim (1993)
5. Manallack, D.T., Livingstone, D.J.: Neural networks in drug discovery: have they lived up to their promise? European Journal of Medicinal Chemistry 34, 95-208 (1999)
6. Nikkilä, J., Törönen, P., Kaski, S., Venna, J., Castrén, E., Wong, G.: Analysis and visualization of gene expression data using Self-Organizing Maps Neural Networks. Special issue on New Developments on Self-Organizing Maps 15(8-9), 953-966 (2002)
7. Fort, J., Letrémy, P., Cottrell, M.: Advantages and drawbacks of the Batch Kohonen algorithm. In: ESANN 2002, pp. 223-230 (2002)
8. Van Hulle, M.: Faithful Representations and Topographic Maps: From Distortionto Information-Based Self-Organization. John Wiley, New York (2000)
9. Fiannaca, A., Di Fatta, G., Gaglio, S., Rizzo, R., Urso, A.: Improved SOM Learning using Simulated Annealing. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D.P. (eds.) ICANN 2007. LNCS, vol. 4668, pp. 279-288. Springer, Heidelberg (2007)
10. Golub, G.H., Van Loan, C.F.: Matrix Computations, 3rd edn. Johns Hopkins University Press, Baltimore (1996)
11. Wall, M.E., Rechtsteiner, A., Rocha, L.M.: Singular value decomposition and principal component analysis. In: Berrar, D.P., Dubitzky, W., Granzow, M. (eds.) A Practical Approach to Microarray Data Analysis, pp. 91-109. Kluwer, Norwell (2003)
12. Di Fatta, G., Fiannaca, A., Rizzo, R., Urso, A., Berthold, M., Gaglio, S.: Context- Aware Visual Exploration of Molecular Datab. In: ICDM Workshops 2006. Sixth IEEE International Conference on Data Mining, Hong Kong, pp. 136-141 (2006)
13. Papadimitriou, C.H., Raghavan, P., Tamaki, H., Vempala, S.: Latent semantic indexing: A probabilistic analysis. In: Proc. 17th ACM Symp. on the Principles of Database Systems, pp. 159-168 (1998)
14. Cullum, J.K., Willoughby, R.A.: Real rectangular matrices. In: Lancozos algorithms for large symmetric eigenvalue computations, Theory, vol. 1. Birkhäuser, Boston (1985)
15. Holt, F.B.: Subspace Representations of Unstructured Text. In: IEEE ICDMWorkshop on Text Mining (TextDM 2001), San Jose, California, USA (2001)
16. Mangasarian, O.L., Street, W.N., Wolberg, W.H.: Breast cancer diagnosis and prognosis via linear programming. Operations Research 43(4), 570-577 (1995)
17. National Cancer Institute, Self-organized map (som) of compounds tested in the nci anti-hiv screen, http://cactus.nci.nih.gov/services/somqsar/