Data selection with set-membership affine projection algorithm

Akram Zardadi; Akram Zardadi

doi:10.3934/ElectrEng.2019.4.359

AIMS Electronics and Electrical Engineering

2019, Volume 3, Issue 4: 359-369. doi: 10.3934/ElectrEng.2019.4.359

Previous Article Next Article

Research article

Data selection with set-membership affine projection algorithm

Akram Zardadi ^,

Department of Mathematics, Payame Noor University (PNU), P.O. Box, 19395-4697, Tehran, Iran

Received: 17 August 2019 Accepted: 31 October 2019 Published: 13 November 2019

In this paper, the set-membership affine projection (SM-AP) algorithm is utilized to censor non-informative data in big data applications. To this end, the probability distribution of the additive noise signal and the excess of mean-squared error (EMSE) in steady-state are employed in order to estimate the threshold parameter of the single threshold SM-AP (ST-SM-AP) algorithm aiming at attaining the desired update rate. Furthermore, by defining an acceptable range for the error signal, the double threshold SM-AP (DT-SM-AP) algorithm is proposed to detect very large errors due to the irrelevant data such as outliers. The DT-SM-AP algorithm can censor non-informative and irrelevant data in big data applications, and it can improve misalignment and convergence rate of the learning process with high computational efficiency. The simulation and numerical results corroborate the superiority of the proposed algorithms over traditional algorithms.
- adaptive filtering,
- set-membership filtering,
- affine projection,
- data censoring,
- big data,
- outliers
Citation: Akram Zardadi. Data selection with set-membership affine projection algorithm[J]. AIMS Electronics and Electrical Engineering, 2019, 3(4): 359-369. doi: 10.3934/ElectrEng.2019.4.359

Related Papers:

Abstract

In this paper, the set-membership affine projection (SM-AP) algorithm is utilized to censor non-informative data in big data applications. To this end, the probability distribution of the additive noise signal and the excess of mean-squared error (EMSE) in steady-state are employed in order to estimate the threshold parameter of the single threshold SM-AP (ST-SM-AP) algorithm aiming at attaining the desired update rate. Furthermore, by defining an acceptable range for the error signal, the double threshold SM-AP (DT-SM-AP) algorithm is proposed to detect very large errors due to the irrelevant data such as outliers. The DT-SM-AP algorithm can censor non-informative and irrelevant data in big data applications, and it can improve misalignment and convergence rate of the learning process with high computational efficiency. The simulation and numerical results corroborate the superiority of the proposed algorithms over traditional algorithms.

References

[1]	Han S, De Maio S, Carotenuto V, et al. (2018) Censoring outliers in radar data: an approximate ML approach and its analysis. IEEE T Aero Elec Sys 55: 534–546.
[2]	Diniz PSR, Yazdanpanah H (2017) Data censoring with set-membership algorithms. In: IEEE Global Conference on Signal and Information Processing (GlobalSIP 2017), Montreal, Canada, 121–125.
[3]	Zhu H, Qian H, Luo X, et al. (2018) Adaptive queuing censoring for big data processing. IEEE Signal Proc Let 25: 610–614. doi: 10.1109/LSP.2018.2815006
[4]	Zheng Y, Niu R, Varshney PK (2014) Sequential bayesian estimation with censored data for multi-sensor systems. IEEE T Signal Proces 62: 2626–2641. doi: 10.1109/TSP.2014.2315163
[5]	Msechu EJ, Giannakis GB (2012) Sensor-centric data reduction for estimation with WSNs via censoring and quantization. IEEE T Signal Proces 60: 400–414. doi: 10.1109/TSP.2011.2171686
[6]	Fernández-Bes J, Arroyo-Valles R, Cid-Sueiro J (2011) Cooperative data censoring for energy- efficient communications in sensor networks. In: IEEE International Workshop on Machine Learning for Signal Processing, Santander, Spain, 1–6.
[7]	Msechu EJ, Giannakis GB (2011) Decentralized data selection for MAP estimation: a censoring and quantization approach. In: 14th International Conference on Information Fusion, Chicago, IL, USA, 1–8.
[8]	Yazdanpanah H, Diniz PSR, Lima MVS (2016) A simple set-membership affine projection algorithm for sparse system modeling. In: 24th European Signal Processing Conference (EUSIPCO 2016), Budapest, Hungary, 1798–1802.
[9]	Yazdanpanah H, Diniz PSR (2017) Recursive least-squares algorithms for sparse system modeling. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2017), New Orleans, LA, USA, 3879–3883.
[10]	Diniz PSR (2013) Adaptive Filtering: Algorithms and Practical Implementation, 4th edition, New York, USA, Springer.
[11]	Gollamudi S, Nagaraj S, Kapoor S, et al. (1998) Set-membership filtering and a set-membership normalized LMS algorithm with an adaptive step size. IEEE Signal Proc Let 5: 111–114. doi: 10.1109/97.668945
[12]	Gollamudi S, Kapoor S, Nagaraj S, et al. (1998) Set-membership adaptive equalization and updator-shared implementation for multiple channel communications systems. IEEE T Signal Proces 46: 2372–2385. doi: 10.1109/78.709523
[13]	Werner S, Diniz PSR (2001) Set-membership affine projection algorithm. IEEE Signal Proc Let 8: 231–235. doi: 10.1109/97.935739
[14]	Yazdanpanah H, Diniz PSR (2017) New trinion and quaternion set-membership affine projection algorithms. IEEE T Circuits-II 64: 216–220.
[15]	Diniz PSR, Yazdanpanah H (2016) Improved set-membership partial-update affine projection algorithm. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2016), Shanghai, China, 4174–4178.
[16]	Takahashi N, Yamada I (2009) Steady-state mean-square performance analysis of a relaxed set-membership NLMS algorithm by the energy conservation argument. IEEE T Signal Proces 57: 3361–3372. doi: 10.1109/TSP.2009.2020747
[17]	Bhotto MZA, Antoniou A (2012) A robust constrained set-membership affine-projection adaptive-filtering algorithm. IEEE T Signal Proces 60: 73–81. doi: 10.1109/TSP.2011.2170980
[18]	Deller JR (1989) Set-membership identification in digital signal processing. IEEE ASSP Magazine 6: 4–20.
[19]	Nagaraj S, Gollamudi S, Kapoor S, et al. (1999) BEACON: an adaptive set-membership filtering technique with sparse updates, IEEE T Signal Proces 47: 2928–2941.
[20]	Yazdanpanah H, Lima MVS, Diniz PSR (2016) On the robustness of the set-membership NLMS algorithm. In: 9th IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM 2016), Rio de Janeiro, Brazil, 1–5.
[21]	Diniz PSR (2018) On Data-Selective Adaptive Filtering. IEEE T Signal Proces 66: 4239–4252. doi: 10.1109/TSP.2018.2847657
[22]	Lima MVS, Diniz PSR (2013) Steady-state MSE performance of the set-membership affine projection algorithm. Circ Syst Signal Pr 32: 1811–1837. doi: 10.1007/s00034-012-9545-4
[23]	Diniz PSR, Braga RP, Werner S (2006) Set-membership affine projection algorithm for echo cancellation. In: International Symposium on Circuits and Systems (ISCAS 2006), Island of Kos, Greece.
[24]	Papoulis A (1991) Probability, Random Variables, and Stochastic Processes, 3rd edition, McGraw Hill, New York, USA.
[25]	Yazdanpanah H, Lima MVS, Diniz PSR (2017) On the robustness of set-membership adaptive filtering algorithms. EURASIP J Adv Sig Pr 2017: 72. doi: 10.1186/s13634-017-0507-7
[26]	Martins WA, Lima MVS, Diniz PSR, et al. (2017) Optimal constraint vectors for set-membership affine projection algorithms. Signal Process 134: 285–294. doi: 10.1016/j.sigpro.2016.11.025
[27]	Lima MVS, Diniz PSR (2010) Steady-state analysis of the set-membership affine projection algorithm. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2010), Dallas, USA, 3802–3805.

Reader Comments

Your name:*

Email:*
© 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)