Discriminant Analysis by Projection Pursuit
discriminant analysis (DA), principal component analysis (PCA), projection pursuit, minimum covariance determinant (MCD), minimum within covariance de
Abstract
A non-parametric discriminant analysis (projection pursuit by principal component analysis) is discussed and used to compare three robust linear discriminant functions that are based on high breakdown point (of location and covariance matrix ) estimators. The major part of this paper deals with practical application of projection pursuit by principal component. In this study 10 simulated data sets that are binomially distributed and a real data set on the yield of two different progenies of palm tree were used for comparisons. From the findings we concluded that the non-parametric procedure (projection pursuit by principal component) have the highest predictive power among other procedures we considered. S-estimator performed better than the other two estimators when real data is considered, while MCD estimator performed better than MWCD estimator.
Downloads
How to Cite
References
R Bolton, W Krzanowski (1999) A characterization of principal components for projection pursuit. 53(2), 108-109.
C Chork, P Rousseeuw (1992) Integrating a high breakdown option into discriminant analysis in exploration geochemistry. 43, 191-203.
C Croux, C Dehon (2001) Robust linear discriminant analysis using Sestimators. 29(3), 473-493.
J Friedman, J Turkey (1974) Projection pursuit for exploratory data analysis. 23(9), 881-890.
N Gunduz, E Fokoue (2015) Robust classification of high dimension low sample size data.
Douglas Hawkins, Geoffrey Mclachlan (1997) High-Breakdown Linear Discriminant Analysis. 92(437), 136-143.
Xuming He, Wing Fung (2000) High Breakdown Estimation for Multiple Populations with Applications to Discriminant Analysis. 72(2), 151-162.
P Huber (1985) Projection pursuit. 13920, 435-525.
Mia Hubert, Katrien Van Driessen (2004) Fast and robust discriminant analysis. 45(2), 301-320.
A Pires, J Branco (2010) Projection pursuit approach to robust linear discriminant analysis. 101(10), 2464-2485.
J Polzehl (1993) Projection pursuit discriminant analysis.
Valentin Todorov, A Pires (2007) Robust selection of variables in linear discriminant analysis. 15(3), 395-407.
V Todorov, N Neykov, P Neytchev (1990) Robust Selection of Variables in the Discriminant Analysis Based on MVE and MCD Estimators. 193-198.
D Woodruff, D Rocke (1994) Computable robust estimation of multivariate location and shape in high dimension using compound estimators. 89, 888-896.
Published
2015-08-08
Issue
Section
License
Copyright (c) 2015 Authors and Global Journals Private Limited
This work is licensed under a Creative Commons Attribution 4.0 International License.