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Kompleks düzlemde büyük-ölçekli regresyon: Bilgilendirici olmayan verileri çevrimiçi olarak sansürleyen CRLS algoritmalarının başarım analizi

Year 2023, , 349 - 359, 15.04.2023

Engin Cemal Mengüç

https://doi.org/10.28948/ngumuh.1234303

Abstract

Büyük veri akışlarından anlamlı bilgilerin çıkarılması ve öğrenilmesi, toplumların yaşam kalitesinin artırılmasının, bilim ve mühendislik alanında yeni teknolojilerin geliştirilmesinin önünü açmaktadır. Öte yandan, sensör teknolojisindeki son atılımlar, hesaplama gücünün ve bilgisayar belleğinin artan kullanılabilirliği, verilerin sadece reel-değerli olmadığını artık büyük ölçekli kompleks-değerli veri kümeleriyle de başa çıkılması gerektiğini ortaya koymuştur. Bu amaç doğrultusunda, bu çalışmada, son zamanlarda önerilen çevrimiçi sansürleme (online censoring, OC) tabanlı kompleks-değerli özyinelemeli en küçük kareler (OC based complex-valued recursive least squares, OC-CRLS) ve OC tabanlı artırılmış CRLS (OC based augmented CRLS, OC-ACRLS) algoritmalarının başarımları ilk defa büyük ölçekli regresyon problemleri üzerinde detaylı olarak test edilmiş ve literatürde yer alan klasik versiyonları ile karşılaştırılmıştır. Benzetim çalışmaları, OC-CRLS ve OC-ACRLS algoritmalarının, OC mekanizmasının getirmiş olduğu avantajlardan dolayı kompleks düzlemde tanımlanmış olan büyük-ölçekli regresyon problemlerinde eğitim süresini ciddi anlamda kısalttığını ve test başarımını negatif yönde etkilemediğini göstermiştir. Bu da OC-CRLS ve OC-ACRLS algoritmalarının, kompleks düzlemde tanımlanabilen büyük veri akışı uygulamalarında etkin ve güçlü algoritmalar olduğunu kanıtlamıştır.

Keywords

Büyük veri regresyon çevrimiçi sansürleme kompleks-değerli veri özyinelemeli en küçük kareler.

Supporting Institution

Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK)

Project Number

121E324

Thanks

Bu çalışma kısmen Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK) tarafından desteklenmiştir (Proje Numarası: 121E324).

References

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  • K. Slavakis, G. B. Giannakis and G. Mateos, Modeling and optimization for big data analytics: (Statistical) learning tools for our era of data deluge. IEEE Signal Processing Magazine, 31 (5), 18-31, 2014.
  • T. Bengtsson, P. Bickel and B. Li, Curse-of-dimensionality revisited: Collapse of the particle filter in very large scale systems. Probability and Statistics: Essays in Honor of David A. Freedman, 2, 316–334, 2008.
  • M. I. Jordan, On statistics, computation and scalability. Bernoulli, 19 (4), 1378-1390, 2013.
  • J. Mairal, F. Bach, J. Ponce and G. S. Sapiro, Online learning for matrix factorization and sparse coding. Journal of Machine Learning Research, 11, 19-60, 2010.
  • M. Mardani, G. Mateos and G. B. Giannakis, Dynamic anomalography: Tracking network anomalies via sparsity and low rank. IEEE Journal on Selected Topics in Signal Processing, 7 (1), 50-66, 2013.
  • S. Theodoridis, K. Slavakis and I. Yamada, Adaptive learning in a world of projections: A unifying framework for linear and nonlinear classification and regression tasks. IEEE Signal Process, 28, 97-123, 2011.
  • J. Qiu, Q. Wu, Y. Xu and S. Feng, A survey of machine learning for big data processing. Eurasip Journal on Advances in Signal Processing, 1 (67), 1-16, 2016.
  • A. L’Heureux, K. Grolinger, H. F. Elyamany and M. A. M. Capretz, Machine learning with big data: challenges and approaches. IEEE Access, 5, 7776-7797, 2016.
  • K. Slavakis, S. J. Kim, G. Mateos and G. B. Giannakis, Stochastic approximation vis-a-vis online learning for big data analytics. IEEE Signal Proc Mag., 31 (6), 124-129, 2014.
  • D. Berberidis, V. Kekatos and G. B. Giannakis, Online censoring for large-scale regressions with application to streaming big data. IEEE Trans. Signal Process., 64 (15), 3854-3867, 2016.
  • L. Evers and C. M. Messow, Sparse kernel methods for high-dimensional survival data. Bioinformat., 14 (2), 1632-1638, 2008.
  • J. Tobin, Estimation of relationships for limited dependent variables. Econometrica: J. Econometr. Soc., 26 (1), 24-36, 1958.
  • S. Maleki and G. Leus, Censored truncated sequential spectrum sensing for cognitive radio networks. IEEE J. Sel. Areas Commun., 31 (3), 364-378, 2013.
  • T. Amemiya, Tobit models: A survey. J. Econom., 24 (1), 3–61, 1984.
  • E. Msechu and G. B. Giannakis, Sensor-centric data reduction for estimation with WSNs via censoring and quantization. IEEE Trans. Signal Process., 60 (1), 400-414, 2012.
  • K. You, L. Xie and S. Song, Asymptotically optimal parameter estimation with scheduled measurements, IEEE Trans. Signal Process., 61 (14), 3521-3531, 2013.
  • D. Berberidis and G. B. Giannakis, Data sketching for large-scale Kalman filtering. IEEE Trans. Signal Process., 65(14), 3688–3701, 2017.
  • Arroyo-Valles, R. S. Maleki and G. Leus, A censoring strategy for decentralized estimation in energy-constrained adaptive diffusion networks. IEEE 14th Workshop Signal Process. Adv. Wireless Commun., 155–159, 2013.
  • R. Jiang, B. Chen, Fusion of censored decisions in wireless sensor networks. IEEE Trans. Wireless Commun., 4 (6), 2668-2673, 2005.
  • Z. Wang, Z. Yu, Q. Ling, D. Berberidis and G. B. Giannakis, Decentralized rls with data-adaptive censoring for regressions over large-scale networks. IEEE Trans. Signal Process., 66 (6), 1634-1648, 2018.
  • H. Zhu, H. Qian, X. Luo and Y. Yang, Adaptive queuing censoring for big data processing. IEEE Signal Process. Lett., 25 (5), 610-614, 2018.
  • A. O. Sarp, E. C. Mengüç, M. Peker and B. Ç. Güvenç, Data-adaptive censoring for short-term wind speed predictors based on MLP, RNN, and SVM. IEEE Systems Journal, 16 (3), 3625-3634, 2022.
  • J. Ferreira, M. Mendonca and P. S. Diniz, Data selection in neural networks. IEEE Open Journal of Signal Processing, 2, 522-534, 2021.
  • M. J. M. Spelta and W. A. Martins, Normalized lms algorithm and data-selective strategies for adaptive graph signal estimation. Signal Process., 167, 107326, 2020.
  • K. L. Yin, Y. F. Pu and L. Lu, Censored regression distributed functional link adaptive filtering algorithm over nonlinear networks. Signal Process., 190, 108318, 2022.
  • Y. Eren, B. Ç. Güvenç and E. C. Mengüç, Online censoring based acoustic feedback cancellation for wearable hearing aids. 30th Signal Processing and Communications Applications Conference (SIU), 1-4, Karabük, Türkiye, 2022.
  • E. C. Mengüç, S. Çınar, M. Xiang and D. P. Mandic, Online censoring based weighted-frequency fourier linear combiner for estimation of pathological hand tremors. IEEE Signal Process. Lett., 28, 1460-1464, 2021.
  • P. S. R. Diniz, On data-selective adaptive filtering. IEEE Transactions on Signal Processing, 66 (16), 4239-4252, 2018.
  • A. Stott, S. Kanna and D. P. Mandic, Widely linear complex partial least squares for latent subspace regression. Signal Processing, 152, 350-362, 2018.
  • D. P. Mandic and S. L. Goh, Complex Valued Nonlinear Adaptive Filters: Noncircularity Widely Linear and Neural Models. United Kingdom: Wiley, 2009.
  • T. Adalı, S. Haykin, Adaptive Signal Processing: Next Generation Solutions. Wiley: IEEE Press, 2010.
  • Y. Xia, C. C. Took and D. P. Mandic, An augmented affine projection algorithm for the filtering of noncircular complex signals. Signal Process., 90, 1788-1799, 2010.
  • B. Jelfs, D. P. Mandic and S. C. Douglas, An adaptive approach for the identification of improper complex signals. Signal Process., 92, 335-344, 2012.
  • T. Adalı, P. J. Schreier and L. L. Scharf, Complex-valued signal processing: The proper way to deal with impropriety. IEEE Trans. Signal Process., 59 (11), 5101–5125, 2011.
  • P. J. Schreier and L. L. Scharf, Statistical Signal Processing of Complex- Valued Data: The Theory of Improper and Noncircular Signals (1. Basım). Cambridge, U.K.: Cambridge Univ. Press, 2010.
  • E. C. Mengüç and N. Acır, An augmented complex-valued Lyapunov stability theory based adaptive filter algorithm. Signal Processing., 137, 10-21, 2017.
  • E. C. Mengüç and N. Acır, An augmented complex-valued least-mean kurtosis algorithm for the filtering of noncircular signals. IEEE Transactions on Signal Processing, 66 (2), 438-448, 2018.
  • B. Widrow, J. McCool and M. Ball, The complex LMS algorithm. Proceedings of the IEEE, 63 (4), 719-720, 1975.
  • A. Tarighat and A. H. Sayed, Least mean-phase adaptive filters with application to communications systems. IEEE Signal Processing Letters., 11 (2), 220-223, 2004.
  • S. Haykin, Adaptive Filter Theory (4. Basım). Prentice-Hall, Upper Saddle River: Pearson Education, 2002.
  • A. Khalili, A. Rastegarnia, W. M. Bazzi and Z. Yang, Derivation and analysis of incremental augmented complex least mean square algorithm. IET Signal Process., 9 (4), 312-319, 2014.
  • B. Picinbono and P. Chevalier, Widely linear estimation with complex data. IEEE Trans. Signal Process., 43 (8), 2030-2033, 1995.
  • E. C. Mengüç and N. Acır, Complex-valued least mean kurtosis adaptive filter algorithm. 23rd Signal Process. Commun. Appl. Conf., 325-328, 2016.
  • A. Khalili and A. Rastegarnia, Tracking analysis of augmented complex least mean square algorithm. Int. J. Adapt. Control Signal Process., 30 (1), 106-114, 2016.
  • A. Hakkarainen, J. Werner, K. R. Dandekar and M. Valkama, Analysis and augmented spatial processing for uplink OFDMA MU-MIMO receiver with transceiver I/Q imbalance and external interference. IEEE Trans. Wireless Commun., 15 (5), 3422-3439, 2016.
  • Z. Shan and T. S. P. Yum, A conjugate augmented approach to direction of-arrival estimation. IEEE Trans. Signal Process., 53 (11), 4104-4109, 2005.
  • P. Chevalier and A. Blin, Widely linear MVDR beamformers for the reception of an unknown signal corrupted by noncircular interferences. IEEE Transactions on Signal Processing, 55 (11), 5323-5336, 2007.
  • S. Javidi, D. P. Mandic and A. Cichocki, Complex blind source extraction from noisy mixtures using second-order statistics. IEEE Trans. Circuits Syst. I, 57 (7), 1404-1416, 2010.
  • C. Park, C. C. Took and D. P. Mandic, Augmented complex common spatial patterns for classification of noncircular EEG from motor imagery tasks. IEEE Trans. Neural Syst. Rehab. Eng., 22 (1), 1-10, 2014.
  • H. Li, N. M. Correa, P. A. Rodriguez, V. D. Calhoun and T. Adalı, Application of independent component analysis with adaptive density model to complex-valued fMRI data. IEEE Transactions on Biomedical Engineering, 58 (10), 2794-2803, 2011.
  • Y. Xia, S. C. Douglas and D. P. Mandic, Adaptive frequency estimation in smart grid applications: exploiting noncircularity and widely linear adaptive estimators. IEEE Signal Process., 29 (5), 44-54, 2012.
  • Y. Xia and D. P. Mandic, Widely linear adaptive frequency estimation of unbalanced three-phase power systems,” IEEE Transactions on Instrumentation and Measurement., 61 (1), 74-83, 2012.
  • S. Javidi, S. L. Goh, M. Pedzisz and D. P. Mandic, The augmented complex least mean square algorithm with application to adaptive prediction problems. Proc. 1st IARP Workshop Cogn. Inform. Process., 54-57, 2008.
  • S. Douglas, Widely-linear recursive least-squares algorithm for adaptive beamforming. Proc. the IEEE Int. Conf. Acoustics, Speech and Signal Process., 2041-2044, 2009.
  • B. Jelfs, D. P. Mandic and S. C. Douglas, An adaptive approach for the identification of improper complex signals. Signal Process., 92, 335-344, 2012.
  • C. Jahanchahi, S. Kanna and D. P. Mandic, Complex dual channel estimation: Cost effective widely linear adaptive filtering. Signal Process., 104, 33-42, 2014.
  • A. H. Sayed, Fundamentals of Adaptive Filtering (1. Basım). NJ: Wiley, 2003.
  • E. C. Mengüç, M. Xiang and D. P. Mandic, Online censoring based complex-valued adaptive filters. Signal Processing, 200, 108638, 2022.
  • Kaggle, Fifa 19 Dataset. https://www.kaggle.com/ karangadiya/fifa19, Accessed 16 February 2022.
  • UC Irvine Machine Learning Repository, Block Feedback Dataset. https://archive.ics.uci.edu/ml/data sets/BlogFeedback, Accessed 8 May 2022.

Large-scale regression in the complex domain: Performance analysis of CRLS algorithms censoring noninformative data in an online manner.

Year 2023, , 349 - 359, 15.04.2023

Engin Cemal Mengüç

https://doi.org/10.28948/ngumuh.1234303

Abstract

Extracting and learning meaningful information from big data streams paves the way for improving the quality of life of societies and the development of new technologies in the field of science and engineering. On the other hand, recent advances in sensor technology, increased availability of computing power and computer memory reveal that data is not just real-valued, but large-scale complex-valued datasets must also be dealt with. For this purpose, for the first time in this study, the performances of the recently proposed online censoring (OC) based complex-valued recursive least squares (OC-CRLS) and OC based augmented CRLS (OC-ACRLS) algorithms are tested on large-scale regression problems and compared with those of their classical versions in the literature in detail. Simulation studies show that the OC-CRLS and OC-ACRLS algorithms significantly shorten the training time in large-scale regression problems defined in the complex domain without affecting testing performance in a negative way, due to the advantages of their OC mechanism. This proves that OC-CRLS and OC-ACRLS algorithms are effective and powerful algorithms in big data streaming applications that can be defined in the complex domain.

Keywords

Büyük veri regresyon çevrimiçi sansürleme kompleks-değerli veri özyinelemeli en küçük kareler.

Project Number

121E324

References

  • Z. Han, M. Hong and D. Wang, Signal Processing and Networking for Big Data Applications (1. Basım). United Kingdom: Cambridge Core, 2017.
  • K. Slavakis, G. B. Giannakis and G. Mateos, Modeling and optimization for big data analytics: (Statistical) learning tools for our era of data deluge. IEEE Signal Processing Magazine, 31 (5), 18-31, 2014.
  • T. Bengtsson, P. Bickel and B. Li, Curse-of-dimensionality revisited: Collapse of the particle filter in very large scale systems. Probability and Statistics: Essays in Honor of David A. Freedman, 2, 316–334, 2008.
  • M. I. Jordan, On statistics, computation and scalability. Bernoulli, 19 (4), 1378-1390, 2013.
  • J. Mairal, F. Bach, J. Ponce and G. S. Sapiro, Online learning for matrix factorization and sparse coding. Journal of Machine Learning Research, 11, 19-60, 2010.
  • M. Mardani, G. Mateos and G. B. Giannakis, Dynamic anomalography: Tracking network anomalies via sparsity and low rank. IEEE Journal on Selected Topics in Signal Processing, 7 (1), 50-66, 2013.
  • S. Theodoridis, K. Slavakis and I. Yamada, Adaptive learning in a world of projections: A unifying framework for linear and nonlinear classification and regression tasks. IEEE Signal Process, 28, 97-123, 2011.
  • J. Qiu, Q. Wu, Y. Xu and S. Feng, A survey of machine learning for big data processing. Eurasip Journal on Advances in Signal Processing, 1 (67), 1-16, 2016.
  • A. L’Heureux, K. Grolinger, H. F. Elyamany and M. A. M. Capretz, Machine learning with big data: challenges and approaches. IEEE Access, 5, 7776-7797, 2016.
  • K. Slavakis, S. J. Kim, G. Mateos and G. B. Giannakis, Stochastic approximation vis-a-vis online learning for big data analytics. IEEE Signal Proc Mag., 31 (6), 124-129, 2014.
  • D. Berberidis, V. Kekatos and G. B. Giannakis, Online censoring for large-scale regressions with application to streaming big data. IEEE Trans. Signal Process., 64 (15), 3854-3867, 2016.
  • L. Evers and C. M. Messow, Sparse kernel methods for high-dimensional survival data. Bioinformat., 14 (2), 1632-1638, 2008.
  • J. Tobin, Estimation of relationships for limited dependent variables. Econometrica: J. Econometr. Soc., 26 (1), 24-36, 1958.
  • S. Maleki and G. Leus, Censored truncated sequential spectrum sensing for cognitive radio networks. IEEE J. Sel. Areas Commun., 31 (3), 364-378, 2013.
  • T. Amemiya, Tobit models: A survey. J. Econom., 24 (1), 3–61, 1984.
  • E. Msechu and G. B. Giannakis, Sensor-centric data reduction for estimation with WSNs via censoring and quantization. IEEE Trans. Signal Process., 60 (1), 400-414, 2012.
  • K. You, L. Xie and S. Song, Asymptotically optimal parameter estimation with scheduled measurements, IEEE Trans. Signal Process., 61 (14), 3521-3531, 2013.
  • D. Berberidis and G. B. Giannakis, Data sketching for large-scale Kalman filtering. IEEE Trans. Signal Process., 65(14), 3688–3701, 2017.
  • Arroyo-Valles, R. S. Maleki and G. Leus, A censoring strategy for decentralized estimation in energy-constrained adaptive diffusion networks. IEEE 14th Workshop Signal Process. Adv. Wireless Commun., 155–159, 2013.
  • R. Jiang, B. Chen, Fusion of censored decisions in wireless sensor networks. IEEE Trans. Wireless Commun., 4 (6), 2668-2673, 2005.
  • Z. Wang, Z. Yu, Q. Ling, D. Berberidis and G. B. Giannakis, Decentralized rls with data-adaptive censoring for regressions over large-scale networks. IEEE Trans. Signal Process., 66 (6), 1634-1648, 2018.
  • H. Zhu, H. Qian, X. Luo and Y. Yang, Adaptive queuing censoring for big data processing. IEEE Signal Process. Lett., 25 (5), 610-614, 2018.
  • A. O. Sarp, E. C. Mengüç, M. Peker and B. Ç. Güvenç, Data-adaptive censoring for short-term wind speed predictors based on MLP, RNN, and SVM. IEEE Systems Journal, 16 (3), 3625-3634, 2022.
  • J. Ferreira, M. Mendonca and P. S. Diniz, Data selection in neural networks. IEEE Open Journal of Signal Processing, 2, 522-534, 2021.
  • M. J. M. Spelta and W. A. Martins, Normalized lms algorithm and data-selective strategies for adaptive graph signal estimation. Signal Process., 167, 107326, 2020.
  • K. L. Yin, Y. F. Pu and L. Lu, Censored regression distributed functional link adaptive filtering algorithm over nonlinear networks. Signal Process., 190, 108318, 2022.
  • Y. Eren, B. Ç. Güvenç and E. C. Mengüç, Online censoring based acoustic feedback cancellation for wearable hearing aids. 30th Signal Processing and Communications Applications Conference (SIU), 1-4, Karabük, Türkiye, 2022.
  • E. C. Mengüç, S. Çınar, M. Xiang and D. P. Mandic, Online censoring based weighted-frequency fourier linear combiner for estimation of pathological hand tremors. IEEE Signal Process. Lett., 28, 1460-1464, 2021.
  • P. S. R. Diniz, On data-selective adaptive filtering. IEEE Transactions on Signal Processing, 66 (16), 4239-4252, 2018.
  • A. Stott, S. Kanna and D. P. Mandic, Widely linear complex partial least squares for latent subspace regression. Signal Processing, 152, 350-362, 2018.
  • D. P. Mandic and S. L. Goh, Complex Valued Nonlinear Adaptive Filters: Noncircularity Widely Linear and Neural Models. United Kingdom: Wiley, 2009.
  • T. Adalı, S. Haykin, Adaptive Signal Processing: Next Generation Solutions. Wiley: IEEE Press, 2010.
  • Y. Xia, C. C. Took and D. P. Mandic, An augmented affine projection algorithm for the filtering of noncircular complex signals. Signal Process., 90, 1788-1799, 2010.
  • B. Jelfs, D. P. Mandic and S. C. Douglas, An adaptive approach for the identification of improper complex signals. Signal Process., 92, 335-344, 2012.
  • T. Adalı, P. J. Schreier and L. L. Scharf, Complex-valued signal processing: The proper way to deal with impropriety. IEEE Trans. Signal Process., 59 (11), 5101–5125, 2011.
  • P. J. Schreier and L. L. Scharf, Statistical Signal Processing of Complex- Valued Data: The Theory of Improper and Noncircular Signals (1. Basım). Cambridge, U.K.: Cambridge Univ. Press, 2010.
  • E. C. Mengüç and N. Acır, An augmented complex-valued Lyapunov stability theory based adaptive filter algorithm. Signal Processing., 137, 10-21, 2017.
  • E. C. Mengüç and N. Acır, An augmented complex-valued least-mean kurtosis algorithm for the filtering of noncircular signals. IEEE Transactions on Signal Processing, 66 (2), 438-448, 2018.
  • B. Widrow, J. McCool and M. Ball, The complex LMS algorithm. Proceedings of the IEEE, 63 (4), 719-720, 1975.
  • A. Tarighat and A. H. Sayed, Least mean-phase adaptive filters with application to communications systems. IEEE Signal Processing Letters., 11 (2), 220-223, 2004.
  • S. Haykin, Adaptive Filter Theory (4. Basım). Prentice-Hall, Upper Saddle River: Pearson Education, 2002.
  • A. Khalili, A. Rastegarnia, W. M. Bazzi and Z. Yang, Derivation and analysis of incremental augmented complex least mean square algorithm. IET Signal Process., 9 (4), 312-319, 2014.
  • B. Picinbono and P. Chevalier, Widely linear estimation with complex data. IEEE Trans. Signal Process., 43 (8), 2030-2033, 1995.
  • E. C. Mengüç and N. Acır, Complex-valued least mean kurtosis adaptive filter algorithm. 23rd Signal Process. Commun. Appl. Conf., 325-328, 2016.
  • A. Khalili and A. Rastegarnia, Tracking analysis of augmented complex least mean square algorithm. Int. J. Adapt. Control Signal Process., 30 (1), 106-114, 2016.
  • A. Hakkarainen, J. Werner, K. R. Dandekar and M. Valkama, Analysis and augmented spatial processing for uplink OFDMA MU-MIMO receiver with transceiver I/Q imbalance and external interference. IEEE Trans. Wireless Commun., 15 (5), 3422-3439, 2016.
  • Z. Shan and T. S. P. Yum, A conjugate augmented approach to direction of-arrival estimation. IEEE Trans. Signal Process., 53 (11), 4104-4109, 2005.
  • P. Chevalier and A. Blin, Widely linear MVDR beamformers for the reception of an unknown signal corrupted by noncircular interferences. IEEE Transactions on Signal Processing, 55 (11), 5323-5336, 2007.
  • S. Javidi, D. P. Mandic and A. Cichocki, Complex blind source extraction from noisy mixtures using second-order statistics. IEEE Trans. Circuits Syst. I, 57 (7), 1404-1416, 2010.
  • C. Park, C. C. Took and D. P. Mandic, Augmented complex common spatial patterns for classification of noncircular EEG from motor imagery tasks. IEEE Trans. Neural Syst. Rehab. Eng., 22 (1), 1-10, 2014.
  • H. Li, N. M. Correa, P. A. Rodriguez, V. D. Calhoun and T. Adalı, Application of independent component analysis with adaptive density model to complex-valued fMRI data. IEEE Transactions on Biomedical Engineering, 58 (10), 2794-2803, 2011.
  • Y. Xia, S. C. Douglas and D. P. Mandic, Adaptive frequency estimation in smart grid applications: exploiting noncircularity and widely linear adaptive estimators. IEEE Signal Process., 29 (5), 44-54, 2012.
  • Y. Xia and D. P. Mandic, Widely linear adaptive frequency estimation of unbalanced three-phase power systems,” IEEE Transactions on Instrumentation and Measurement., 61 (1), 74-83, 2012.
  • S. Javidi, S. L. Goh, M. Pedzisz and D. P. Mandic, The augmented complex least mean square algorithm with application to adaptive prediction problems. Proc. 1st IARP Workshop Cogn. Inform. Process., 54-57, 2008.
  • S. Douglas, Widely-linear recursive least-squares algorithm for adaptive beamforming. Proc. the IEEE Int. Conf. Acoustics, Speech and Signal Process., 2041-2044, 2009.
  • B. Jelfs, D. P. Mandic and S. C. Douglas, An adaptive approach for the identification of improper complex signals. Signal Process., 92, 335-344, 2012.
  • C. Jahanchahi, S. Kanna and D. P. Mandic, Complex dual channel estimation: Cost effective widely linear adaptive filtering. Signal Process., 104, 33-42, 2014.
  • A. H. Sayed, Fundamentals of Adaptive Filtering (1. Basım). NJ: Wiley, 2003.
  • E. C. Mengüç, M. Xiang and D. P. Mandic, Online censoring based complex-valued adaptive filters. Signal Processing, 200, 108638, 2022.
  • Kaggle, Fifa 19 Dataset. https://www.kaggle.com/ karangadiya/fifa19, Accessed 16 February 2022.
  • UC Irvine Machine Learning Repository, Block Feedback Dataset. https://archive.ics.uci.edu/ml/data sets/BlogFeedback, Accessed 8 May 2022.
There are 61 citations in total.

Details

Primary Language Turkish
Subjects Electrical Engineering
Journal Section Electrical and Electronics Engineering
Authors

Engin Cemal Mengüç 0000-0002-0619-549X

Project Number 121E324
Publication Date April 15, 2023
Submission Date January 15, 2023
Acceptance Date February 25, 2023
Published in Issue Year 2023

Cite

APA Mengüç, E. C. (2023). Kompleks düzlemde büyük-ölçekli regresyon: Bilgilendirici olmayan verileri çevrimiçi olarak sansürleyen CRLS algoritmalarının başarım analizi. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 12(2), 349-359. https://doi.org/10.28948/ngumuh.1234303
AMA Mengüç EC. Kompleks düzlemde büyük-ölçekli regresyon: Bilgilendirici olmayan verileri çevrimiçi olarak sansürleyen CRLS algoritmalarının başarım analizi. NÖHÜ Müh. Bilim. Derg. April 2023;12(2):349-359. doi:10.28948/ngumuh.1234303
Chicago Mengüç, Engin Cemal. “Kompleks düzlemde büyük-ölçekli Regresyon: Bilgilendirici Olmayan Verileri çevrimiçi Olarak sansürleyen CRLS algoritmalarının başarım Analizi”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 12, no. 2 (April 2023): 349-59. https://doi.org/10.28948/ngumuh.1234303.
EndNote Mengüç EC (April 1, 2023) Kompleks düzlemde büyük-ölçekli regresyon: Bilgilendirici olmayan verileri çevrimiçi olarak sansürleyen CRLS algoritmalarının başarım analizi. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 12 2 349–359.
IEEE E. C. Mengüç, “Kompleks düzlemde büyük-ölçekli regresyon: Bilgilendirici olmayan verileri çevrimiçi olarak sansürleyen CRLS algoritmalarının başarım analizi”, NÖHÜ Müh. Bilim. Derg., vol. 12, no. 2, pp. 349–359, 2023, doi: 10.28948/ngumuh.1234303.
ISNAD Mengüç, Engin Cemal. “Kompleks düzlemde büyük-ölçekli Regresyon: Bilgilendirici Olmayan Verileri çevrimiçi Olarak sansürleyen CRLS algoritmalarının başarım Analizi”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 12/2 (April 2023), 349-359. https://doi.org/10.28948/ngumuh.1234303.
JAMA Mengüç EC. Kompleks düzlemde büyük-ölçekli regresyon: Bilgilendirici olmayan verileri çevrimiçi olarak sansürleyen CRLS algoritmalarının başarım analizi. NÖHÜ Müh. Bilim. Derg. 2023;12:349–359.
MLA Mengüç, Engin Cemal. “Kompleks düzlemde büyük-ölçekli Regresyon: Bilgilendirici Olmayan Verileri çevrimiçi Olarak sansürleyen CRLS algoritmalarının başarım Analizi”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, vol. 12, no. 2, 2023, pp. 349-5, doi:10.28948/ngumuh.1234303.
Vancouver Mengüç EC. Kompleks düzlemde büyük-ölçekli regresyon: Bilgilendirici olmayan verileri çevrimiçi olarak sansürleyen CRLS algoritmalarının başarım analizi. NÖHÜ Müh. Bilim. Derg. 2023;12(2):349-5.

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