Abstract
This paper aims at proposing a learning theory approach to the topic of estimating transfer functions in system identification. A frequency domain identification problem is formulated as an atomic norm regularization scheme in a random design framework of learning theory. Such a formulation makes it possible to obtain sparsity and provide finite sample estimates for learning the transfer function in a learning theory framework. Error analysis is done for the learning algorithm by applying a local polynomial reproduction formula, concentration inequalities and iteration techniques. The convergence rate obtained here is the best in the literature. It is hoped that the learning theory approach to the frequency domain identification problem would bring new ideas and lead to more interactions among the areas of system identification, learning theory and frequency analysis.
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References
Chen, T., Ohlsson, H., Ljung, L.: On the estimation of transfer functions, regularizations and Gaussian processes - Revisited. Automatica 48, 1525–1535 (2012)
Cucker, F., Zhou, D.X.: Learning Theory: An Approximation Theory Viewpoint. Cambridge University Press, Cambridge (2007)
Efron, B., Hastie, T., Johnstone, I., Tibshirani, R.: Least angle regression. Ann. Stat. 32, 407–499 (2004)
Gröchenig, K.: Foundations of Time-Frequency Analysis. Birkhäuser, Boston (2001)
Heckel, R., Bölcskei, H.: Identification of sparse linear operators. IEEE Trans. Inform. Theory 59, 7985–8000 (2013)
Hu, T., Fan, J., Wu, Q., Zhou, D.X.: Regularization schemes for minimum error entropy principle. Anal. Appl. (2014). doi:10.1142/S0219530514500110
Jetter, K., Stöckler, J., Ward, J.D.: Error estimates for scattered data interpolation on spheres. Math. Comput. 68, 733–747 (1999)
Kailath, T.: Measurements on time-variant communication channels. IEEE Trans. Inform. Theory 8, 229–236 (1962)
Ljung, L.: Perspectives on system identification. Ann. Rev. Control 34, 1–12 (2010)
Ljung, L.: System Identification: Theory for the User, 2nd edn. Prentice-Hall, Englewood Cliffs (1999)
Pfander, G.E., Walnut, D.F.: Measurement of time-variant linear channels. IEEE Trans. Inform. Theory 52, 4808–4820 (2006)
Pillonetto, G., Minh, H.Q., Chiuso, A.: A new kernel-based approach for nonlinear system identification. IEEE Trans. Autom. Control 56, 2825–2840 (2011)
Pillonetto, G., Nicolao, G.D.: A new kernel-based approach for linear system identification. Automatica 46, 81–93 (2010)
Shah, P., Bhaskar, B.N., Tang, G., Recht, B.: Linear system identification via atomic norm regularization. In: Proceedings of the 51st IEEE Conference on Decision and Control, Maui, Hawaii, USA, pp. 6265–6270 (2012)
Shi, L., Feng, Y.L., Zhou, D.X.: Concentration estimates for learning with \(\ell ^1\)-regularizer and data dependent hypothesis spaces. Appl. Comput. Harmon. Anal. 31, 286–302 (2011)
Smale, S., Zhou, D.X.: Shannon sampling and function reconstruction from point values. Bull. Am. Math. Soc. 41, 279–305 (2004)
Smale, S., Zhou, D.X.: Learning theory estimates via integral operators and their approximations. Constr. Approx. 26, 153–172 (2007)
Steinwart, I., Scovel, C.: Fast rates for support vector machines using Gaussian kernels. Ann. Stat. 35, 575–607 (2007)
Sun, H.W., Wu, Q.: Indefinite kernel networks with dependent sampling. Anal. Appl. 11, 1350020 (2013). 15 pages
Tibshirani, R.: Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58, 267–288 (1996)
Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)
Vidyasagar, M., Karandikar, R.L.: A learning theory approach to system identification and stochastic adaptive control. J. Process Control 18, 421–430 (2008)
Wang, H.Y., Xiao, Q.W., Zhou, D.X.: An approximation theory approach to learning with \(\ell ^1\) regularization. J. Approx. Theory 167, 240–258 (2013)
Wendland, H.: Local polynomial reproduction and moving least squares approximation. IMA J. Numer. Anal. 21, 285–300 (2001)
Wendland, H.: Scattered Data Approximation. Cambridge Monographs on Applied and Computational Mathematics, vol. 17. Cambridge University Press, Cambridge (2005)
Wu, Q., Ying, Y., Zhou, D.X.: Learning rates of least-square regularized regression. Found. Comput. Math. 6, 171–192 (2006)
Wu, Q., Zhou, D.X.: Learning with sample dependent hypothesis spaces. Comput. Math. Appl. 56, 2896–2907 (2008)
Xiao, Q.W., Zhou, D.X.: Learning by nonsymmetric kernels with data dependent spaces and \(\ell ^1\)-regularizer. Taiwan. J. Math. 14, 1821–1836 (2010)
Zhao, P., Yu, B.: On model selection consistency of Lasso. J. Mach. Learn. Res. 7, 2541–2563 (2006)
Zhou, D.X.: Capacity of reproducing kernel spaces in learning theory. IEEE Trans. Inform. Theory 49, 1743–1752 (2003)
Zhou, D.X.: Derivative reproducing properties for kernel methods in learning theory. J. Comput. Appl. Math. 220, 456–463 (2008)
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The work described in this paper is supported partially by the Research Grants Council of Hong Kong [Project No. CityU 105011] and by National Natural Science Foundation of China under Grants 11371007 and 11461161006.
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Communicated by Gitta Kutyniok.
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Li, L., Zhou, DX. Learning Theory Approach to a System Identification Problem Involving Atomic Norm. J Fourier Anal Appl 21, 734–753 (2015). https://doi.org/10.1007/s00041-015-9389-y
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DOI: https://doi.org/10.1007/s00041-015-9389-y
Keywords
- Learning theory
- System identification
- Transfer function estimation
- Frequency domain identification
- Atomic norm regularization