Commenced in January 2007
Paper Count: 30222
Sparsity-Aware Affine Projection Algorithm for System Identification
Authors: Young-Seok Choi
Abstract:This work presents a new type of the affine projection (AP) algorithms which incorporate the sparsity condition of a system. To exploit the sparsity of the system, a weighted l1-norm regularization is imposed on the cost function of the AP algorithm. Minimizing the cost function with a subgradient calculus and choosing two distinct weighting for l1-norm, two stochastic gradient based sparsity regularized AP (SR-AP) algorithms are developed. Experimental results exhibit that the SR-AP algorithms outperform the typical AP counterparts for identifying sparse systems.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1339165Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1039
 S. Haykin, Adaptive filter theory, Upper Saddle River, NJ: Prentice Hall, 2002.
 A. H. Sayed, Fundamentals of adaptive filtering, New York: Wiley, 2003.
 K. Ozeki and T. Umeda, “An adaptive filtering algorithm using an orthogonal projection to an affine subspace and its properties,” Electro. Commun. Jpn., vol. 67-A, no. 5, pp. 19–27, 1984.
 O. Hoshuyama, R. A. Goubran, and A. Sugiyama, “A generalized proportionate variable step-size algorithm for fast changing acoustic environments,” in Proc. Int. Conf. on Acoustics, Speech, and Signal Process. (ICASSP 2004), pp. IV-161–IV-164, 2004
 C. Paleologu, S. Ciochina, and J. Benesty, “An efficient proportionate affine projection algorithm for echo cancellation,” IEEE Signal Process. Lett., vol. 17, no. 29, pp. 165–168, Feb. 2010.
 Y. Chen, Y. Gu, and A. O. Hero, “Sparse LMS for system identification,” in Proc. Int. Conf. on Acoustics, Speech, and Signal Process. (ICASSP 2009), pp. 3125–3128, 2009.
 Y. Gu, J. Jin, and S. Mei, “l0 norm constraint LMS algorithm for sparse system identification,” IEEE Signal Process. Lett., vol. 16, no. 9, pp. 774–777, Sep. 2009.
 Y.-S. Choi and W.-J. Song, “Noise constrained data-reusing adaptive filtering algorithms for system identification,” IEICE Trans. Fundamentals., vol. E.95-A, no. 6, pp. 1084–1087, June. 2012.
 D. Bertsekas, A. Nedic, and A. Ozdaglar, Convex analysis and optimization, Athena Scientific, Cambridge, MA USA, 2003.
 E. J. Candes, M. B. Wakin, and S. P. Boyd, “Enhancing sparsity by reweighting l0 Minimization,” J. Fourier Anal. Appl., vol. 14, pp. 877–905, 2008.