A co-training algorithm for multi-view data with applications in data fusion

Journal of Chemometrics

Volumn 23, Issue 6, 2009, Pages 294-303

  Culp, Mark ^a   Michailidis, George ^b  

^a WEST VIRGINIA UNIVERSITY   (United States)

^b UNIVERSITY OF MICHIGAN   (United States)

Author keywords

Co training; Data fusion; Multi view learning; Partial least squares; Random forests

Indexed keywords

DIAGNOSIS; LEAST SQUARES APPROXIMATIONS; MOLECULES; PRINCIPAL COMPONENT ANALYSIS;

APPLICATION DATA; CO-TRAINING; CO-TRAINING ALGORITHM; DRUG DISCOVERY APPLICATIONS; MULTI-VIEW DATUM; MULTI-VIEW LEARNING; PARTIAL LEAST-SQUARES; RANDOM FORESTS; SCIENTIFIC APPLICATIONS; SOURCE VIEW;

DATA FUSION;

EID: 67649878314     PISSN: 08869383     EISSN: 1099128X     Source Type: Journal    
DOI: 10.1002/cem.1233     Document Type: Article

References (21)

1. Leach A, Gillet V. An Introduction to Chemoinformatics. Kluwer Academic Publishers: London, 2003.
2. Lundblad R. Chemical Reagents for Protein Modification. CRC Press Inc.: Boca Raton, 2004.
3. Mitchell H. Multi-sensor Data Fusion-An Introduction. Springer-Verlag: Berlin, 2007.
4. Jansen J, Bro R, Hoefsloot H, Van den Berg F, Westerhuis J, Smilde A. Parafasca: Asca combined with parafac for the analysis of metabolic fingerprinting data. J. Chemometrics 2008; 22(2): 114-121.
5. Yamanishi Y, Vert J, Kanehisa M. Protein network inference from multiple genomic data: a supervised approach. Bioinformatics 2004; 20(1): 363-370.
6. Daszykowski M,Wu W, Nicholls A,Ball R,CzekajT, WalczakB. Identifying potential biomarkers in lc-ms data. J. Chemometrics 2007; 21(7): 292-302.
7. Jorgensen K, Segtnan V, Naes T. A comparison of methods for analysing regression models with both spectral and designed variables. J. Chemometrics 2004; 18:451-464.
8. Blum A, Mitchell T. Combining labeled and unlabeled data with co-training. In Proceedings of the 11th annual conference on computational learning theory, 1998; 92-100.
9. Abney S. Understanding the yarowsky algorithm. Comput. Linguist. 2004; 30(3): 365-395.
10. De Jong S, Ter Braak C. Comments on the pls kernel algorithm. J. Chemometrics 1994; 8:169-174.
11. Levina E, Wagaman A, Callender A, Mandair G, Morris M. Estimating the number of pure chemical components in a mixture by maximum likelihood. J. Chemometrics 2007; 21:24-34.
12. Culp M, Michailidis G. An iterative algorithm for extending learners to a semi-supervised setting. J. Comput. Graph. Stat. 2008; 17(3): 545-571.
13. Brefeld U, Scheffer T. Co-em support vector learning. Int. Conf. Mach. Learn. 2004; 21:16.
14. Mevik B, Wehrens R.The pls package: principal component and partial least squares regression in r. J. Stat. Softw. 2007; 18(2): 1-24.
15. Leardi R. Application of genetic algorithm-pls for feature selection in spectral data sets. J. Chemometrics 2000; 14(5): 643-655.
16. Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning (Data Mining, Inference and Prediction). Springer Verlag: New York, 2001.
17. Faraway J. Modeling continuous shape change for facial animation. Stat. and Comput. 2004; 14:357-363.
18. Caetano S, Ustun B, Hennessy S, Smeyers-Verbeke J, Melssen W, Downey G, Buydens L, Heyden Y. Geographical classification of olive oils by the application of cart and svm to their ft-ir. J. Chemometrics 2007; 21:324-334.
19. Breiman L. Random forests. Mach. Learn. 2001; 45(1): 5-32.
20. Hunter W. Rational drug design:a multidiciplinary approach.Mol. Med. Today 1995; 1(1): 31-34.
21. Liaw A, Wiener M. Classification and regression by randomforest. R News 2002; 2(3): 18-22.