Binary variable extraction using nonlinear principal component analysis in classical location model

Binary variable extraction using nonlinear principal component analysis in classical location model

Location model is a predictive classification model that determines the groups of objects which contain mixed categorical and continuous variables. The simplest location model is known as classical location model, which can be constructed easily using maximum likelihood estimation. This model perfor...

Full description

Saved in:
Bibliographic Details
Main Author: Long, Mei Mei
Format: Thesis
Language:eng
eng
Published: 2016
Subjects:
QA273-280 Probabilities
Mathematical statistics
QA299.6-433 Analysis
Online Access:https://etd.uum.edu.my/6007/1/s817093_01.pdf
https://etd.uum.edu.my/6007/2/s817093_02.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
id my-uum-etd.6007
record_format uketd_dc
institution Universiti Utara Malaysia
collection UUM ETD
language eng
eng
advisor Hamid, Hashibah
Aziz, Nazrina
topic QA273-280 Probabilities
Mathematical statistics
QA299.6-433 Analysis
spellingShingle QA273-280 Probabilities
Mathematical statistics
QA299.6-433 Analysis
Long, Mei Mei
Binary variable extraction using nonlinear principal component analysis in classical location model
description Location model is a predictive classification model that determines the groups of objects which contain mixed categorical and continuous variables. The simplest location model is known as classical location model, which can be constructed easily using maximum likelihood estimation. This model performs ideally with few binary variables. However, there is an issue of many empty cells when it involves a large number of binary variables, b due to the exponential growth of multinomial cells by 2b. This issue affects the classification accuracy badly when no information can be obtained from the empty cells to estimate the required parameters. This issue can be solved by implementing the dimensionality reduction approach into the classical location model. Thus, the objective of this study is to propose a new classification strategy to reduce the large binary variables. This can be done by integrating classical location model and nonlinear principal component analysis where the binary variables reduction is based on variance accounted for, VAF. The proposed location model was tested and compared to the classical location model using leave-one-out method. The results proved that the proposed location model could reduce the number of empty cells and has better performance in term of misclassification rate than the classical location model. The proposed model was also validated using a real data. The findings showed that this model was comparable or even better than the existing classification methods. In conclusion, this study demonstrated that the new proposed location model can be an alternative method in solving the mixed variable classification problem, mainly when facing with a large number of binary variables.
format Thesis
qualification_name masters
qualification_level Master's degree
author Long, Mei Mei
author_facet Long, Mei Mei
author_sort Long, Mei Mei
title Binary variable extraction using nonlinear principal component analysis in classical location model
title_short Binary variable extraction using nonlinear principal component analysis in classical location model
title_full Binary variable extraction using nonlinear principal component analysis in classical location model
title_fullStr Binary variable extraction using nonlinear principal component analysis in classical location model
title_full_unstemmed Binary variable extraction using nonlinear principal component analysis in classical location model
title_sort binary variable extraction using nonlinear principal component analysis in classical location model
granting_institution Universiti Utara Malaysia
granting_department Awang Had Salleh Graduate School of Arts & Sciences
publishDate 2016
url https://etd.uum.edu.my/6007/1/s817093_01.pdf
https://etd.uum.edu.my/6007/2/s817093_02.pdf
_version_ 1747828005849268224
spelling my-uum-etd.60072021-04-05T03:18:36Z Binary variable extraction using nonlinear principal component analysis in classical location model 2016 Long, Mei Mei Hamid, Hashibah Aziz, Nazrina Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts and Sciences QA273-280 Probabilities. Mathematical statistics QA299.6-433 Analysis Location model is a predictive classification model that determines the groups of objects which contain mixed categorical and continuous variables. The simplest location model is known as classical location model, which can be constructed easily using maximum likelihood estimation. This model performs ideally with few binary variables. However, there is an issue of many empty cells when it involves a large number of binary variables, b due to the exponential growth of multinomial cells by 2b. This issue affects the classification accuracy badly when no information can be obtained from the empty cells to estimate the required parameters. This issue can be solved by implementing the dimensionality reduction approach into the classical location model. Thus, the objective of this study is to propose a new classification strategy to reduce the large binary variables. This can be done by integrating classical location model and nonlinear principal component analysis where the binary variables reduction is based on variance accounted for, VAF. The proposed location model was tested and compared to the classical location model using leave-one-out method. The results proved that the proposed location model could reduce the number of empty cells and has better performance in term of misclassification rate than the classical location model. The proposed model was also validated using a real data. The findings showed that this model was comparable or even better than the existing classification methods. In conclusion, this study demonstrated that the new proposed location model can be an alternative method in solving the mixed variable classification problem, mainly when facing with a large number of binary variables. 2016 Thesis https://etd.uum.edu.my/6007/ https://etd.uum.edu.my/6007/1/s817093_01.pdf text eng public https://etd.uum.edu.my/6007/2/s817093_02.pdf text eng public masters masters Universiti Utara Malaysia Al-Ani, A., & Deriche, M. (2002). A new technique for combining multiple classifiers using the dempster-shafer theory of evidence. Journal of Artificial Intelligence Research, 17, 333–361. Albanis, G. T., & Batchelor, R. A. (2007). Combining heterogeneous classifiers for stock selection. Intelligent Systems in Accounting, Finance and Management, 15(1-2), 1–21. doi:10.1002/isaf Alrawashdeh, M. J., Sabri, S. R. M., & Ismail, M. T. (2012). Robust linear discriminant analysis with financial ratios in special interval. Applied Mathematical Sciences, 6(121), 6021–6034. Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609. doi:10.1111/j.1540-6261.1968.tb00843.x Anderson, J. A. (1972). Separate sample logistic discrimination. Biometrika, 59(1), 19–35. Asparoukhov, O., & Krzanowski, W. J. (2000). Non-parametric smoothing of the location model in mixed variable discrimination. Statistics and Computing, 10, 289–297. Banerjee, S., & Pawar, S. (2013). Predicting consumer purchase intention: A discriminant analysis approach. NMIMS Management Review, XXIII, 113–129. Bar-Hen, A., & Daudin, J. J. (2007). Discriminant analysis based on continuous and discrete variables. In Statistical Methods for Biostatistics and Related Fields (pp. 3–27). Springer Berlin Heidelberg. doi:10.1007/978-3-540-32691-5_1 Basu, A., Bose, S., & Purkayastha, S. (2004). Robust discriminant analysis using weighted likelihood estimators. Journal of Statistical Computation & Simulation, 74(6), 445–460. Berardi, V. L., & Zhang, G. P. (1999). The effect of misclassificaton costs on neural network classifiers. Decision Sciences, 30(3), 659–682. Berchuck, A., Iversen, E. S., Luo, J., Clarke, J., Horne, H., Levine, D. A., … Lancaster, J. M. (2009). Microarray analysis of early stage serous ovarian cancers shows profiles predictive of favorable outcome. Clinical Cancer Research: An Official Journal of the American Association for Cancer Research, 15(7), 2448–2455. doi:10.1158/1078-0432.CCR-08-2430 Betz, N. E. (1987). Use of discriminant analysis in counseling psychology research. Journal of Counseling Psychology, 34(4), 393–403. doi:10.1037/0022-0167.34.4.393 Birzer, M. L., & Craig-Moreland, D. E. (2008). Using discriminant analysis in policing research. Professional Issues in Criminal Justice, 3(2), 33–48. Blasius, J., & Gower, J. C. (2005). Multivariate prediction with nonlinear principal components analysis: Application. Quality and Quantity, 39, 373–390. doi:10.1007/s11135-005-3006-0 Braga-Neto, U. M., & Dougherty, E. R. (2004). Is cross-validation valid for smallsample microarray classification? Bioinformatics, 20(3), 374–380. doi:10.1093/bioinformatics/btg419 Braga-Neto, U. M., Hashimoto, R., Dougherty, E. R., Nguyen, D. V, & Carroll, R. J. (2004). Is cross-validation better than resubstitution for ranking genes? Bioinformatics, 20(2), 253–258. doi:10.1093/bioinformatics/btg399 Brito, I., Celeux, G., & Ferreira, A. S. (2006). Combining methods in supervised classification: A comparative study on discrete and continuous problems. Revstat - Statistical Journal, 4(3), 201–225. Retrieved from http://www.ine.pt/revstat/pdf/rs060302.pdf Carakostas, M. C., Gossett, K. A., Church, G. E., & Cleghorn, B. L. (1986). Veterinary Pathology Online. Veterinary Pathology, 23, 254–269. doi:10.1177/030098588602300306 Chang, P. C., & Afifi, A. A. (1974). Classification based on dichotomous and continuous variables. Journal of the American Statistical Association, 69(346), 336–339. Chen, K., Wang, L., & Chi, H. (1997). Methods of combining multiple classifiers with different features and their applications to text-independent speaker identification. International Journal of Pattern Recognition and Artificial Intelligence, 11(3), 417–445. doi:10.1142/S0218001497000196 Cochran, W. G., & Hopkins, C. E. (1961). Some classification problems with multivariate qualitative data. Biometrics, 17(1), 10–32. Costa, P. S., Santos, N. C., Cunha, P., Cotter, J., & Sousa, N. (2013). The use of multiple correspondence analysis to explore associations between categories of qualitative variables in healthy ageing. Journal of Aging Research, 1–12. doi:10.1155/2013/302163 Crook, J. N., Edelman, D. B., & Thomas, L. C. (2007). Recent developments in consumer credit risk assessment. European Journal of Operational Research, 183, 1447–1465. doi:10.1016/j.ejor.2006.09.100 Daudin, J. J. (1986). Selection of variables in mixed-variable discriminant analysis. Biometrics, 42(3), 473–481. Daudin, J. J., & Bar-Hen, A. (1999). Selection in discriminant analysis with continuous and discrete variables. Computational Statistics and Data Analysis, 32, 161–175. doi:10.1016/S0167-9473(99)00027-4 Day, N. E., & Kerridge, D. F. (1967). A general maximum likelihood discriminant. Biometrics, 23(2), 313–323. De Leeuw, J. (2006). Nonlinear principal component analysis and related techniques. In J. B. M Greenacre (Ed.), Multiple Correspondence Analysis and Related Methods (pp. 107–133). Chapman and Hall, Boca Raton, FA. doi:10.1109/IJCNN.2003.1223477 De Leeuw, J. (2011). Nonlinear principal component analysis and related techniques. Department of Statistics, UCLA. Retrieved from https://escholarship.org/uc/item/7bt7j6nk De Leeuw, J. (2013). History of nonlinear principal component analysis, 1–14. De Leeuw, J., & Mair, P. (2009). Gifi methods for optimal scaling in R: The package homals. Journal of Statistical Software, 31(4), 1–21. Retrieved from http://www.jstatsoft.org/ de Leon, A. R., Soo, A., & Williamson, T. (2011). Classification with discrete and continuous variables via general mixed-data models. Journal of Applied Statistics, 38(5), 1021–1032. doi:10.1080/02664761003758976 Deakin, E. B. (1972). A discriminant analysis of predictors of business failure. Journal of Accountin Research, 10(1), 167–179. Donoho, D. L. (2000). High-dimensional data analysis: The curses and blessings of dimensionality. AMS Math Challenges Lecture, 1–33. Retrieved from http://mlo.cs.man.ac.uk/resources/Curses.pdf Dray, S. (2008). On the number of principal components: A test of dimensionality based on measurements of similarity between matrices. Computational Statistics and Data Analysis, 52, 2228–2237. doi:10.1016/j.csda.2007.07.015 Eisenbeis, R. A. (1977). Pitfalls in the application of discriminant analysis in Business, Finance, and Economics. The Journal of Finance, 32(3), 875–900. Everitt, B. S. & Merette, C. (1990). The Clustering of Mixed-mode Data: A Comparison of Possible Approaches. Journal of Applied Statistics, 17, 283-297. Fan, J., & Fan, Y. (2008). High dimensional classification using features annealed indepence rules. Annals of Statistics, 36(6), 2605-2637. doi:10.1214/07-AOS504.High Fan, J., & Lv, J. (2010). A selective overview of variable selection in high dimensional feature space. Statistica Sinica, 20(1), 101-148. doi:10.1063/1.3520482 Ferrari, P. A., & Manzi, G. (2010). Nonlinear principal component analysis as a tool for the evaluation of customer satisfaction. Quality Technology and Quantitative Management, 7(2), 117–132. Retrieved from http://air.unimi.it/handle/ 2434/141402\nhttp://web2.cc.nctu.edu.tw/~qtqm/ qtqmpapers/2010V7N2/2010V7N2_F2.pdf Ferrari, P. A., & Salini, S. (2011). Complementary use of rasch models and nonlinear principal components analysis in the assessment of the opinion of Europeans about utilities. Journal of Classification, 28, 53–69. doi:10.1007/s00357-011-9081-0 Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, 179–188. Gifi, A. (1990). Nonlinear multivariate analysis. Chichester, England: Wiley. Glick, N. (1973). Sample-based multinomial classification. Biometrics, 29(2), 241–256. Goulermas, J. Y., Findlow, A. H., Nester, C. J., Howard, D., & Bowker, P. (2005). Automated design of robust discriminant analysis classifier for foot pressure lesions using kinematic data. IEEE Transactions on Biomedical Engineering, 52(9), 1549–1562. doi:10.1109/TBME.2005.851519 Greenland, S. (1988). Variance estimation for epidemiologic effect estimates under misclassification. Statistics in Medicine, 7(7), 745–757. doi:10.1002/sim.4780070704 Guo, Y., Hastie, T., & Tibshirani, R. (2007). Regularized linear discriminant analysis and its application in microarrays. Biostatistics, 8(1), 86–100. doi:10.1093/biostatistics/kxj035 Gupta, V. (2013). Exploring data generated by pocket devices. London. Retrieved from http://files.howtolivewiki.com/SMART_CITIES/The_ Smart_City.To_Whos_Advantage.Pocket_Devices_and_ Data_Trails.Vinay_Gupta.pdf Hamid, H. (2010). A new approach for classifying large number of mixed variables. International Scholarly and Scientific Research and Innovation, 4(10), 120–125. doi:14621 Hamid, H. (2014). Integrated smoothed location model and data reduction approaches for multi variables classification. Unpublished Doctoral Dissertation. Universiti Utara Malaysia. Hamid, H., & Mahat, N. I. (2013). Using principal component analysis to extract mixed variables for smoothed location model. Far East Journal of Mathematical Sciences (FJMS), 80(1), 33–54. Hand, D. J. (2006). Classifier technology and the illusion of progress. Statistical Science, 21(1), 1–15. doi:10.1214/088342306000000079 Holden, J. E., Finch, W. H., & Kelley, K. (2011). A comparison of two-group classification methods. Educational and Psychological Measurement, 71(5), 870–901. doi:10.1177/0013164411398357 Holden, J. E., & Kelley, K. (2010). The effects of initially misclassified data on the effectiveness of discriminant function analysis and finite mixture modeling. Educational and Psychological Measurement, 70(1), 36–55. doi:10.1177/0013164409344533 Hothorn, T., & Lausen, B. (2003). Double-bagging: Combining classifiers by bootstrap aggregation. Pattern Recognition, 36, 1303–1309. doi:10.1016/S0031-3203(02)00169-3 Hsiao, C., & Chen, H. (2010). On classification from the view of outliers. IAENG International Journal of Computer Science, 37(4), 1–9. Retrieved from http://arxiv.org/abs/0907.5155 Jin, H., & Kim, S. (2015). Performance evaluations of diagnostic prediction with neural networks with data filters in different types. International Journal of Bio-Science and Bio-Technology, 7(1), 61–70. doi:http://dx.doi.org/10.14257/ijbsbt.2015.7.1.07 Katz, M. H. (2011). Multivariate analysis: A practical guide for clinicians and public health researchers. Cambridge: Cambridge University Press. Kim, K., Aronov, P., Zakharkin, S. O., Anderson, D., Perroud, B., Thompson, I. M., & Weiss, R. H. (2009). Urine metabolomics analysis for kidney cancer detection and biomarker discovery. Molecular & Cellular Proteomics: MCP, 8(3), 558–570. doi:10.1074/mcp.M800165-MCP200 Knoke, J. D. (1982). Discriminant analysis with discrete and continuous variables. Biometrics, 38(1), 191–200. Kristensen, P. (1992). Bias from nondifferential but dependent misclassification of exposure and outcome. Epidemiology, 3(3), 210–215. Retrieved from http://www.jstor.org/stable/3703154 Krzanowski, W. J. (1975). Discrimination and classification using both binary and continuous variables. Journal of American Statistical Association, 70(352), 782–790. Krzanowski, W. J. (1979). Some linear transformations for mixtures of binary and continuous variables, with particular reference to linear discriminant analysis. Biometrika, 66(1), 33–39. doi:10.1093/biomet/66.1.33 Krzanowski, W. J. (1980). Mixtures of continuous and categorical variables in discriminant analysis. Biometrics, 36(3), 493–499. Krzanowski, W. J. (1982). Mixtures of continuous and categorical variables in discriminant analysis: A hypothesis testing approach. Biometrics, 38(4), 991-1002. Krzanowski, W. J. (1983). Stepwise location model choice in mixed-variable discrimination. Journal of the Royal Statistical Society. Series C (Applied Statisitcs), 32(3), 260–266. Krzanowski, W. J. (1993). The location model for mixtures of categorical and continuous variables. Journal of Classification, 10(1), 25–49. doi:10.1007/BF02638452 Krzanowski, W. J. (1995). Selection of variables, and assessment of their performance, in mixed-variable discriminant analysis. Computational Statistics & Data Analysis, 19, 419–431. doi:10.1016/0167-9473(94)00011-7 Lachenbruch, P. A., & Goldstein, M. (1979). Discriminant analysis. Biometrics, 35(1), 69–85. LeBlanc, M., & Tibshirani, R. (1996). Combining estimates in regression and classification. Journal of the American Statistical Association, 91(436), 1641–1650. doi:10.1080/01621459.1996.10476733 Lee, S., Huang, J. Z., & Hu, J. (2010). Sparse logistic principal components analysis for binary data. Annals of Applied Statistics, 4(3), 1579–1601. doi:10.1016/j.biotechadv.2011.08.021.Secreted Li, Q. (2006). An integrated framework of feature selection and extraction for appearance-based recognition. Unpublished Doctoral Dissertation. University of Delaware Newark, DE, USA. Li, Q., & Racine, J. (2003). Nonparametric estimation of distributions with categorical and continuous data. Journal of Multivariate Analysis, 86, 266–292. doi:10.1016/S0047-259X(02)00025-8 Li, X., & Ye, N. (2006). A supervised clustering and classification algorithm for mining data with mixed variables. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 36(2), 396–406. doi:10.1109/TSMCA.2005.853501 Lillvist, A. (2009). Observations of social competence of children in need of special support based on traditional disability categories versus a functional approach. Early Child Development and Care, 180(9), 1129–1142. doi:10.1080/03004430902830297 Linting, M. (2007). Nonparametric inference in nonlinear principal components analysis: Exploration and beyond. Doctoral Thesis, Leiden University. Retrieved from http://hdl.handle.net/1887/12386 Linting, M., Meulman, J. J., Groenen, P. J. F., & van der Kooij, A. J. (2007a). Nonlinear principal components analysis: Introduction and application. Psychological Methods, 12(3), 336–358. doi:10.1037/1082-989X.12.3.336 Linting, M., Meulman, J. J., Groenen, P. J. F., & van der Kooij, A. J. (2007b). Stability of nonlinear principal components analysis: An empirical study using the balanced bootstrap. Psychological Methods, 12(3), 359–379. doi:10.1037/1082-989X.12.3.359 Linting, M., & van der Kooij, A. J. (2012). Nonlinear principal components analysis with CATPCA: A tutorial. Journal of Personality Assessment, 94(1), 12–25. doi:10.1080/00223891.2011.627965 Linting, M., van Os, B. J., & Meulman, J. J. (2011). Statistical significance of the contribution of variables to the PCA solution: An alternative premutation strategy. Psychometrika, 76(3), 440–460. Little, R. J. A., & Schluchter, M. D. (1985). Maximum likelihood estimation for mixed continuous and categorical data with missing values. Biometrika, 72(3), 497–512. Lombardo, R., & Meulman, J. J. (2010). Multiple correspondence analysis via polynomial transformations of ordered categorical variables. Journal of Classification, 27, 191–210. doi:10.1007/s00357-010- Maclaren, W. M. (1985). Using discriminant analysis to predict attacks of complicated pneumoconiosis in coalworkers. Journal of the Royal Statistical Society, Series D (The Statistician), 34(2), 197–208. Mahat, N. I. (2006). Some investigations in discriminant analysis with mixed variables. Unpublished Doctoral Dissertation. University of Exeter, London, UK. Mahat, N. I., Krzanowski, W. J., & Hernandez, A. (2007). Variable selection in discriminant analysis based on the location model for mixed variables. Advances in Data Analysis and Classification, 1(2), 105–122. doi:10.1007/s11634-007-0009-9 Mahat, N. I., Krzanowski, W. J., & Hernandez, A. (2009). Strategies for nonparametric smoothing of the location model in mixed-variable discriminant analysis. Modern Applied Science, 3(1), 151–163. Mair, P., & Leeuw, J. De. (2008). Rank and set restrictions for homogeneity analysis in R: The “homals” package. In JSM 2008 Proceedings, Statistical Computing Section. (pp. 2142–2149). Manisera, M., Dusseldorp, E., & van der Kooij, A. J. (2005). Component structure of job satisfaction based on Herzberg’s theory. Retrieved May 9, 2015, from http://www.datatheory.nl/pages/fullmanuscript_ final_epm.pdf Manisera, M., van der Kooij, A. J., & Dusseldorp, E. (2010). Identifying the component structure of job satisfaction by nonlinear principal components analysis. Quality Technology and Quantitative Management, 7, 97–115. Retrieved from http://elisedusseldorp.nl/pdf/Manisera_QTQM2010.pdf Marian, N., Villarroya, A., & Oller, J. M. (2003). Minimum distance probability discriminant analysis for mixed variables. Biometrics, 59, 248–253. Markos, A. I., Vozalis, M. G., & Margaritis, K. G. (2010). An optimal scaling approach to collaborative filtering using categorical principal component analysis and neighborhood formation. In Artificial Intelligence Applications and Innovations (pp. 22-29). Springer Berlin Heidelberg. Meulman, J. J. (1992). The integration of multidimensional scaling and multivariate analysis with optimal transformations. Psychometrika, 57(4), 539–565. Meulman, J. J. (2003). Prediction and classification in nonlinear data analysis: Something old, something new, something borrowed, something blue. Psychometrika, 68(4), 493–517. Meulman, J. J., van der Kooij, A. J., & Heiser, W. J. (2004). Principal components analysis with nonlinear optimal scaling transformations for ordinal and nominal data. In D. Kaplan (Ed.), Handbook of Quantitative Methods in the Social Sciences (pp. 49–70). Newbury Park, CA: Sage Publications. doi:10.4135/9781412986311 Moustaki, I., & Papageorgiou, I. (2005). Latent class models for mixed variables with applications in Archaeometry. Computational Statistics and Data Analysis, 48(3), 659–675. doi:10.1016/j.csda.2004.03.001 Nasios, N., & Bors, A. G. (2007). Kernel-based classification using quantum mechanics. Pattern Recognition, 40, 875–889. doi:10.1016/j.patcog.2006.08.011 Nishisato, S., & Arri, P. S. (1975). Nonlinear programming approach to optimal scaling of partially ordered categories. Psychometrika, 40(4), 525–548. doi:10.1007/BF02291554 Olkin, I., & Tate, R. F. (1961). Multivariate correlation models with discrete and continuous variables. The Annals of Mathematical Statistics 32, 448–465. Olosunde, A. A., & Soyinka, A. T. (2013). Discrimination and classification of poultry feeds data. International Journal of Mathematical Research, 2(5), 37–41. doi:10.1080/00207390600819003 Peres-Neto, P. R., Jackson, D. A., & Somers, K. M. (2005). How many principal components? Stopping rules for determining the number of non-trivial axes revisited. Computational Statistics and Data Analysis, 49, 974–997. doi:10.1016/j.csda.2004.06.015 Poon, W.-Y. (2004). Identifying influence observations in discriminant analysis. Statistical Methods in Medical Reserach, 13, 291–308. doi:10.1191/0962280204sm367ra Prokop, M., & Řezanková, H. (2011). Data dimensionality reduction methods for ordinal data. International Days of Statistics and Economics, Prague, 523–533. Prokop, M., & Řezanková, H. (2013). Comparison of dimensionality reduction methods applied to ordinal data. The Seventh International Days of Statistics and Economics, Prague, 1150–1159. Ramadevi, G. N., & Usharaani, K. (2013). Study on dimensionality reduction techniques and applications. Publications of Problems & Application in Engineering Research, 4(1), 134–140. Russom, P. (2013). Managing big data. Washington. Schein, a I., Saul, L. K., & Ungar, L. H. (2003). A generalized linear model for principal component analysis of binary data. In Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics. Retrieved from http://research.microsoft.com/conferences/aistats 2003/proceedings/papers.htm Schmitz, P. I. M., Habbema, J. D. F., & Hermans, J. (1983). The performance of logistic discrimination on myocardial infarction data, in comparison with some other discriminant analysis methods. Statistics in Medicine, 2(2), 199–205. Simon, R., Radmacher, M. D., Dobbin, K., & McShane, L. M. (2003). Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. Journal of the National Cancer Institute, 95(1), 14–18. Solanas, A., Manolov, R., Leiva, D., & Richard’s, M. M. (2011). Retaining principal components for discrete variables. Anuario de Psicologia, 41(1-3), 33–50. Takane, Y., Bozdogan, H., & Shibayama, T. (1987). Ideal point dicriminant analysis. Psychometrika, 52(3), 371–392. Titterington, D. M., Murray, G. D., Murray, L. S., Speigelhalter, D. J., Skene, A. M., Habbema, J. D. F., & Gelpke, G. J. (1981). Comparison of discrimination techniques applied to a complex data set of head injured patients. Journal of the Royal Statistical Society A, 144(2), 145–175. Retrieved from http://www.jstor.org/stable/2981918 van Heerden, C., Barnard, E., Davel, M., van der Walt, C., van Dyk, E., Feld, M., & Muller, C. (2010). Combining regression and classification methods for improving automatic speaker age recognition. In Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference (pp. 5174–5177). IEEE. doi:10.1109/ICASSP.2010.5495006 Vlachonikolis, I. G., & Marriott, F. H. C. (1982). Discrimination with mixed binary and continuous data. Journal of the Royal Statistical Society, Series C (Applied Statistics), 31(1), 23–31. Wernecke, K.-D. (1992). A coupling procedure for the discrimination of mixed data. Biometrics, 48(2), 497–506. Wernecke, K.-D., Unger, S., & Kalb, G. (1986). The use of combined classifiers in medical functional diagnostics. Biometrical Journal, 28(1), 81–88. doi:10.1002/bimj.4710280116 Xu, L., Krzyzak, A., & Suen, C. Y. (1992). Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Transactions on Systems, Man and Cybernetics, 22(3), 418–435. doi:10.1109/21.155943 Yang, Y. (2005). Can the strangths of AIC and BIC be shared? A conflict between model identification and regression estimation. Biometrika, 92(4), 937–950. Retrieved from http://www.jstor.org/stable/20441246 Young, P. D. (2009). Dimension reduction and missing data in statistical discrimination. Unpublished Doctoral Dissertation. USA Baylor University. Zhang, M. Q. (2000). Discriminant analysis and its application in DNA sequence motif recognition. Briefings in Bioinformatics, 1(4), 1–12. doi:10.1093/bib/1.4.331 Zheng, H., & Zhang, Y. (2008). Feature selection for high-dimensional data in astronomy. Advances in Space Research, 41(12), 1960–1964. doi:10.1016/j.asr.2007.08.033

Similar Items