Abstract
The paper investigates capabilities of fast orthogonal neural networks in a feature extraction task for classification problems. Neural networks with an architecture based on the fast cosine transform, type II and IV are built and applied for extraction of features used as a classification base for a multilayer perceptron. The results of the tests show that adaptation of the neural network allows to obtain a better transform in the feature extraction sense as compared to the fast cosine transform. The neural implementation of both the feature extractor and the classifier enables integration and joint learning of both blocks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Osowski, S.: Neural networks for information processing (in Polish). OWPW, Warsaw (2000)
Osowski, S., Nghia, D.D.: Fourier and wavelet descriptors for shape recognition using neural networks - a comparative study. Pattern Recognition 35, 1949–1957 (2002)
Stasiak, B., Yatsymirskyy, M.: Application of Fourier-Mellin Transform To Categorization of 3D Objects (in Polish). In: Proc. of the III Conference on Information Technologies, Gdańsk, Poland (2005)
Sikora, T., Makai, B.: Shape-adaptive DCT for generic coding of video. IEEE Trans. on Circuits Syst. Video Technol. 5, 59–62 (1995)
Pan, Z., Rust, A., Bolouri, H.: Image Redundancy Reduction for Neural Network Classification using Discrete Cosine Transforms. In: Proc. of the International Joint Conference on Neural Networks, Como, Italy, vol. 3, pp. 149–154 (2000)
Jacymirski, M., Szczepaniak, P.S.: Neural realization of fast linear filters. In: Proc. of the 4th EURASIP - IEEE Region 8 International Symposium on Video/Image Processing and Multimedia Communications, pp. 153–157 (2002)
Stasiak, B., Yatsymirskyy, M.: Fast orthogonal neural networks. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 142–149. Springer, Heidelberg (2006)
Rao, K.R., Yip, P.: Discrete cosine transform. Academic Press, San Diego (1990)
Jacymirski, M.: Fast homogeneous algorithms of cosine transforms, type II and III with tangent multipliers (in Polish). In: Automatics, vol. 7, pp. 727–741. AGH University of Science and Technology Press, Cracow (2003)
Stasiak, B., Yatsymirskyy, M.: Recursive learning of fast orthogonal neural networks. In: Proc. of the International Conf. on Signals and Electronic Systems, pp. 653–656 (2006)
Rutkowski, L.: Methods and techniques of artificial intelligence (in Polish). Polish Scientific Publishers PWN, Warszawa (2005)
Hettich, S., Bay, S.D.: The UCI KDD Archive. University of California, Department of Information and Computer Science, Irvine, CA (1999), http://kdd.ics.uci.edu
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Stasiak, B., Yatsymirskyy, M. (2007). On Feature Extraction Capabilities of Fast Orthogonal Neural Networks. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2007. Lecture Notes in Computer Science, vol 4432. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71629-7_4
Download citation
DOI: https://doi.org/10.1007/978-3-540-71629-7_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71590-0
Online ISBN: 978-3-540-71629-7
eBook Packages: Computer ScienceComputer Science (R0)