Parametric embedding for class visualization

Advances in Neural Information Processing Systems

Volumn , Issue , 2005, Pages

  Iwata, Tomoharu ^a   Saito, Kazumi ^a   Ueda, Naonori ^a   Stromsten, Sean ^b   Griffiths, Thomas L ^b   Tenenbaum, Joshua B ^b  

^a NTT Communication Science Laboratories   (Japan)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; STATISTICS;

COMPUTATIONAL ADVANTAGES; EMBEDDING METHOD; GAUSSIAN MIXTURES; KULLBACK LEIBLER DIVERGENCE; LATENT DIRICHLET ALLOCATION; LOW-DIMENSIONAL SPACES; OBJECT-RELATIONS; UNSUPERVISED ALGORITHMS;

VECTOR SPACES;

EID: 84899002783     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (7)

1. D. Blei, A. Ng and M. Jordan. Latent dirichlet allocation. NIPS 15, 2002.
2. V. de Silva, J. B. Tenenbaum. Global versus local methods in nonlinear dimensionality reduction. NIPS 15, pp. 705-712, 2002.
3. R. Fisher. The use of multiple measurements in taxonomic problem. Annuals of Eugenics 7, pp.179-188, 1950.
4. G. Hinton and S. Roweis. Stochastic neighbor embedding. NIPS 15, 2002.
5. I.T. Joliffe. Principal Component Analysis. Springer, 1980.
6. J. Tenenbaum, V. de Silva and J. Langford. A global geometric framework for nonlinear dimensionality reduction. Science 290 pp. 2319-2323, 2000.
7. W. Torgerson. Theory and Methods of Scaling. New York, Wiley, 1958.