Dimensionality reduction of clustered data sets

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 30, Issue 3, 2008, Pages 535-540

  Sanguinetti, Guido ^a  

^a UNIVERSITY OF SHEFFIELD   (United Kingdom)

Author keywords

Clustering; Dimensionality reduction; Discriminant analysis; Probabilistic algorithms

Indexed keywords

CLUSTERING ALGORITHMS; DATABASE SYSTEMS; DISCRIMINANT ANALYSIS; MAXIMUM LIKELIHOOD ESTIMATION; PROBABILITY; UNSUPERVISED LEARNING;

CLASSIFICATION ALGORITHM; DATA SETS; LINEAR DISCRIMINANT ANALYSIS; PROBABILISTIC ALGORITHM;

DATA REDUCTION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; CLUSTER ANALYSIS; FACTUAL DATABASE; INFORMATION RETRIEVAL; MATHEMATICAL COMPUTING; METHODOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CLUSTER ANALYSIS; DATABASES, FACTUAL; INFORMATION STORAGE AND RETRIEVAL; NUMERICAL ANALYSIS, COMPUTER-ASSISTED; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 39549101749     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2007.70819     Document Type: Article

References (15)

1. C.M. Bishop, Pattern Recognition and Machine Learning. Springer, 2006.
2. C.M. Bishop and M.E. Tipping, "A Hierarchical Latent Variable Model for Data Visualisation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 3, pp. 281-293, Mar. 1998.
3. C.M. Bishop, M. Svensen, and C.K.I. Williams, "GTM: The Generative Topographic Mapping," Neural Computation, vol. 10, no. 1, pp. 215-234, 1998.
4. R.A. Fisher, "The Use of Multiple Measurements in Taxonomic Problems," Annals of Eugenics, vol. 7, pp. 179-188, 1936.
5. A.P. Dempster, N.M. Laird, and D.B. Rubin, "Maximum Likelihood from Incomplete Data via the EM Algorithm," J. Royal Statistical Soc. B vol. 39, no. 1, pp. 1-38, 1977.
6. M. Girolami and R. Breitling, "Biologically Valid Linear Factor Models of Gene Expression," Bioinformatics, vol. 20, no. 17, pp. 3021-3033, 2004.
7. M. Girolami, "Latent Class and Trait Models for Data Classification and Visualisation," Independent Component Analysis: Principles and Practice, Cambridge Univ. Press, 2001.
8. T.R. Golub, D.K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J.P. Mesirov, H. Coller, M.L. Loh, J.R. Downing, M.A. Caligiuri, C.D. Bloomfield, and E.S. Lander, "Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring," Science, vol. 286, no. 5439, pp. 531-537, 1999.
9. G. Sanguinetti, M. Milo, M. Rattray, and N.D. Lawrence, "Accounting for Probe-Level Noise in Principal Component Analysis of Microarray Data," Bioinformatics, vol. 21, no. 19, pp. 3748-3754, 2005.
10. N.D. Lawrence, "Probabilistic Non-Linear Principal Component Analysis with Gaussian Process Latent Variable Models," J. Machine Learning Research, vol. 6, pp. 1783-1816, 2005.
11. M. Tipping and C.M. Bishop, "Probabilistic Principal Component Analysis," J. Royal Statistical Soc. B, vol. 21, no. 3, pp. 611-622, 1999.
12. M. Tipping and C.M. Bishop, "Mixtures of Probabilistic Principal Component Analyzers," Neural Computation, vol. 11, no. 2, pp. 443-482, 1999.
13. C.M. Bishop, "Bayesian PCA," Proc. Advances in Neural Information Processing Systems, 1999.
14. M. Welling, F. Agakov, and C.K.I. Williams, "Extreme Component Analysis," Proc. Advances in Neural Information Processing Systems 2003.
15. S. Dasgupta, "Learning Mixture of Gaussians," Proc. 40th Ann. IEEE Symp. Foundations of Computer Science, 1999.