Quantized kernel recursive minimum error entropy algorithm
In this paper, we propose a online vector quantization (VQ) method based on the kernel recursive minimum error entropy (KRMEE) algorithm. According to information theoretic learning (ITL), the minimum error entropy criterion (MEE) is robust and can effective resistance to non-Gaussian noise. By comb...
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| Vydané v: | Engineering applications of artificial intelligence Ročník 121; s. 105957 |
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01.05.2023
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| Abstract | In this paper, we propose a online vector quantization (VQ) method based on the kernel recursive minimum error entropy (KRMEE) algorithm. According to information theoretic learning (ITL), the minimum error entropy criterion (MEE) is robust and can effective resistance to non-Gaussian noise. By combining the kernel recursive least squares (KRLS) algorithm with MEE criterion, KRMEE algorithm has been generated, which has excellent performance in non-Gaussian environments. However, with the size of data increases, the computational complexity will raise. We propose a quantized to solve this problem, the input space of the algorithm is quantized to suppress the linear growth radial basis function (RBF) network in kernel adaptive filtering (KAF). The VQ method is different from novelty criterion (NC), approximate linear dependency (ALD) criterion, and other sparsity methods, the online VQ method need to construct the dictionary, and calculate the distance by Euclidean norm. We propose a novel quantized kernel recursive minimum error entropy (QKRMEE) algorithm by combining VQ method with KRMEE algorithm, and update the solution with a recursive algorithm. In Mackey-Glass time series and a real-world datasets, Monte Carlo simulation experiments show that the proposed algorithm achieves better predictive performance in non-Gaussian noise environment. Meanwhile, the algorithm can restrain the growth of RBF network well, thus reducing the computational complexity and memory consumption effectively. |
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| AbstractList | In this paper, we propose a online vector quantization (VQ) method based on the kernel recursive minimum error entropy (KRMEE) algorithm. According to information theoretic learning (ITL), the minimum error entropy criterion (MEE) is robust and can effective resistance to non-Gaussian noise. By combining the kernel recursive least squares (KRLS) algorithm with MEE criterion, KRMEE algorithm has been generated, which has excellent performance in non-Gaussian environments. However, with the size of data increases, the computational complexity will raise. We propose a quantized to solve this problem, the input space of the algorithm is quantized to suppress the linear growth radial basis function (RBF) network in kernel adaptive filtering (KAF). The VQ method is different from novelty criterion (NC), approximate linear dependency (ALD) criterion, and other sparsity methods, the online VQ method need to construct the dictionary, and calculate the distance by Euclidean norm. We propose a novel quantized kernel recursive minimum error entropy (QKRMEE) algorithm by combining VQ method with KRMEE algorithm, and update the solution with a recursive algorithm. In Mackey-Glass time series and a real-world datasets, Monte Carlo simulation experiments show that the proposed algorithm achieves better predictive performance in non-Gaussian noise environment. Meanwhile, the algorithm can restrain the growth of RBF network well, thus reducing the computational complexity and memory consumption effectively. |
| ArticleNumber | 105957 |
| Author | Chen, Shanmou He, Yue Gao, Yuyi Jiang, Wang |
| Author_xml | – sequence: 1 givenname: Wang orcidid: 0000-0001-5542-3471 surname: Jiang fullname: Jiang, Wang email: j15856001008@163.com – sequence: 2 givenname: Yuyi surname: Gao fullname: Gao, Yuyi – sequence: 3 givenname: Yue surname: He fullname: He, Yue – sequence: 4 givenname: Shanmou surname: Chen fullname: Chen, Shanmou |
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| Cites_doi | 10.1109/TSP.2016.2539127 10.1109/LSP.2015.2428713 10.1016/j.sigpro.2020.107836 10.1016/j.dsp.2015.09.015 10.1016/j.sigpro.2021.108410 10.1109/SPAWC.2012.6292933 10.1109/TNN.2010.2050212 10.1016/j.sigpro.2020.107810 10.1109/TSP.2007.907881 10.1109/LSP.2017.2761886 10.1109/TSP.2017.2669903 10.1016/j.physd.2008.07.006 10.1016/j.sigpro.2019.02.030 10.1016/j.sigpro.2020.107534 10.1016/j.engappai.2020.103797 10.1109/TSP.2004.830985 10.1109/TNN.2004.836241 10.1090/S0002-9947-1950-0051437-7 10.1109/TNNLS.2013.2258936 10.1109/TNN.2009.2033676 10.1109/TNNLS.2018.2868812 10.1016/j.sigpro.2020.107712 10.1109/TNNLS.2011.2178446 10.1162/neco.1991.3.2.213 10.1162/neco.1995.7.2.219 |
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| Keywords | Quantized kernel recursive minimum error entropy Kernel recursive minimum error entropy Online prediction |
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