Abstract
In this paper, we propose a new feature extraction method for regression problems. It is a modified version of linear discriminant analysis (LDA) which is a very successful feature extraction method for classification problems. In the proposed method, the between class and the within class scatter matrices in LDA are modified so that they fit in regression problems. The samples with small differences in the target values are used to constitute the within class scatter matrix while the ones with large differences in the target values are used for the between class scatter matrix. We have applied the proposed method in estimating the head pose and compared the performance with the conventional feature extraction methods.
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References
Cios, K.J., Pedrycz, W., Swiniarski, R.W.: Data Mining Methods for Knowledge Discovery, ch. 9. Kluwer Academic Publishers, Dordrecht (1998)
Joliffe, I.T.: Principal Component Analysis. Springer, Heidelberg (1986)
Bell, A.J., Sejnowski, T.J.: An Information-Maximization Approach to Blind Separation and Blind Deconvolution. Neural Computation 7, 1129–1159 (1995)
Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, New York (1990)
Weisberg, S.: Applied Linear Regression, ch. 3, 2nd edn., p. 324. John Wiley, New York (1985)
Loog, M.: Supervised Dimensionality Reduction and Contextual Pattern Recognition in Medical Image Processing, ch. 3. Ponsen & Looijen, Wageningen, The Netherlands (2004)
Li, K.C.: Sliced Inverse Regression for Dimension Reduction (with discussioin). J. the American Statistical Association. 86, 316–342 (1991)
Li, K.C.: On Principal Hessian Directions for Data Visualization and Dimension Reduction: Another Application of Stein’s lemma. J. the American Statistical Assiciation. 87, 1025–1039 (1992)
Kwak, N., Kim, C.: Dimensionality reduction based on ICA for regression problems. In: Proc. Int’l Conf. on Artificial Neural Networks (IJCNN), pp. 1–10 (2006)
Sim, T., Baker, S., Bsat, M.: The CMU Pose, Illumination, and Expression Database. IEEE Trans. Pattern Analysis and Machine Intelligence 25, 1615–1618 (2003)
Georghiades, A.S., Belhumeur, P.N.: Frome Few to Many: Illumination Cone Models for Face Recognition Under Variable Lighting and Pose. IEEE Trans. Pattern Analysis and Machine Intelligence 23, 643–660 (2001)
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Kwak, N., Choi, SI., Choi, CH. (2008). Feature Extraction for Regression Problems and an Example Application for Pose Estimation of a Face. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2008. Lecture Notes in Computer Science, vol 5112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69812-8_43
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DOI: https://doi.org/10.1007/978-3-540-69812-8_43
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