Interpretable dimension reduction

Journal of Applied Statistics

Volumn 32, Issue 9, 2005, Pages 969-987

  Chipman, Hugh A ^a   Gu, Hong ^b  

^a ACADIA UNIVERSITY   (Canada)

^b DALHOUSIE UNIVERSITY   (Canada)

Author keywords

Data mining; Dimension reduction; Homogeneity; Interpretable; Principal component; Sparsity; Stepwise algorithm

Indexed keywords

EID: 31344446168     PISSN: 02664763     EISSN: None     Source Type: Journal    
DOI: 10.1080/02664760500168648     Document Type: Article

References (10)

1. Cadima, J. and Jolliffe, I. (1995) Loadings and correlations in the interpretation of principal components Journal of Applied Statistics, 22, pp. 203-214.
2. Furnival, G. and Wilson, R., Jr. (1974) Regression by Leaps and Bounds Technometrics, 16, pp. 499-511.
3. Hausman, R. (1982) Constrained multivariate analysis. In Optimisation in Statistics. (pp. 137-151). Amsterdam: North Holland.
4. Jolliffe, I. (1989) Rotation of ill-defined principal components Applied Statistics, 38, pp. 139-147.
5. Jolliffe, I. and Uddin, M. (2000) The simplified component technique: An alternative to rotated principal components Journal of Computation and Graphical Statistics, 9, pp. 689-710.
6. Jolliffe, I. and Trendafilov, N. and Uddin, M. (2003) A modified principal component component technique based on the LASSO Journal of Computational and Graphical Statistics, 12, pp. 531-547.
7. Lock, R. (1993) 1993 new car data Journal of Statistics Education, pp. 1. Available online at http://www.amstat.org/publications/jse/
8. Rao, C. (1965) Linear Statistical Inference and Its Applications. New York: Wiley.
9. Tibshirani, R. (1996) Regression shrinkage and selection via the lasso Journal of the Royal Statistical Society: Series B, 58(1), pp. 267-288.
10. Vines, S. (2000) Simple principal components Applied Statistics, 49, pp. 441-451.