Self-organizing kernel adaptive filtering

Zhao, Songlin; Chen, Badong; Cao, Zheng; Zhu, Pingping; Prı́ncipe, José C.

doi:10.1186/s13634-016-0406-3

Cited by 14 publications

(3 citation statements)

References 44 publications

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“…In these methods, a specific criterion is employed to decide whether a particular point, x n , is to be included to the expansion, or (if that point is discarded) how its respective output y n can be exploited to update the remaining weights of the expansion. There are also methods that can remove specific points from the expansion, if their information becomes obsolete, in order to increase the tracking ability of the algorithm [40].…”

Section: B Kernel Online Learningmentioning

confidence: 99%

Online Distributed Learning Over Networks in RKH Spaces Using Random Fourier Features

Bouboulis

Chouvardas

Theodoridis

2018

IEEE Trans. Signal Process.

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We present a novel diffusion scheme for online kernel-based learning over networks. So far, a major drawback of any online learning algorithm, operating in a reproducing kernel Hilbert space (RKHS), is the need for updating a growing number of parameters as time iterations evolve. Besides complexity, this leads to an increased need of communication resources, in a distributed setting. In contrast, the proposed method approximates the solution as a fixed-size vector (of larger dimension than the input space) using Random Fourier Features. This paves the way to use standard linear combine-then-adapt techniques. To the best of our knowledge, this is the first time that a complete protocol for distributed online learning in RKHS is presented. Conditions for asymptotic convergence and boundness of the networkwise regret are also provided. The simulated tests illustrate the performance of the proposed scheme.

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Section: B Kernel Online Learningmentioning

confidence: 99%

Online Distributed Learning Over Networks in RKH Spaces Using Random Fourier Features

Bouboulis

Chouvardas

Theodoridis

2018

IEEE Trans. Signal Process.

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“…the least-mean-square (LMS) or recursive-least-square (RLS) rationales [4]. Due to the simplicity of their implementation and intuitive presentation, KAFs have been used in a number of applications from medicine [5] to telecommunications [6]; moreover, KAF is an active field of research in terms of kernel design [7], [8], [9], automatic determination of model orders [10], and learning approaches [11].…”

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confidence: 99%

Improving sparsity in kernel adaptive filters using a unit-norm dictionary

Tobar

2017

2017 22nd International Conference on Digital Signal Processing (DSP)

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Kernel adaptive filters, a class of adaptive nonlinear time-series models, are known by their ability to learn expressive autoregressive patterns from sequential data. However, for trivial monotonic signals, they struggle to perform accurate predictions and at the same time keep computational complexity within desired boundaries. This is because new observations are incorporated to the dictionary when they are far from what the algorithm has seen in the past. We propose a novel approach to kernel adaptive filtering that compares new observations against dictionary samples in terms of their unit-norm (normalised) versions, meaning that new observations that look like previous samples but have a different magnitude are not added to the dictionary. We achieve this by proposing the unit-norm Gaussian kernel and define a sparsification criterion for this novel kernel. This new methodology is validated on two real-world datasets against standard KAF in terms of the normalised mean square error and the dictionary size.

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“…Recently, the KAF has been widely used in signal processing, such as channel estimation, noise cancellation, and system identification [9][10][11][12][13]. Then, there are some typical nonlinear adaptive filtering algorithms, such as the kernel least mean square (KLMS) algorithm [14], the kernel affine projection algorithm [15], the kernel recursive least square (KRLS) algorithm [16], and many others [17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

A Quantized Kernel Learning Algorithm Using a Minimum Kernel Risk-Sensitive Loss Criterion and Bilateral Gradient Technique

Luo

Deng

Wang

et al. 2017

Entropy

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Abstract:Recently, inspired by correntropy, kernel risk-sensitive loss (KRSL) has emerged as a novel nonlinear similarity measure defined in kernel space, which achieves a better computing performance. After applying the KRSL to adaptive filtering, the corresponding minimum kernel risk-sensitive loss (MKRSL) algorithm has been developed accordingly. However, MKRSL as a traditional kernel adaptive filter (KAF) method, generates a growing radial basis functional (RBF) network. In response to that limitation, through the use of online vector quantization (VQ) technique, this article proposes a novel KAF algorithm, named quantized MKRSL (QMKRSL) to curb the growth of the RBF network structure. Compared with other quantized methods, e.g., quantized kernel least mean square (QKLMS) and quantized kernel maximum correntropy (QKMC), the efficient performance surface makes QMKRSL converge faster and filter more accurately, while maintaining the robustness to outliers. Moreover, considering that QMKRSL using traditional gradient descent method may fail to make full use of the hidden information between the input and output spaces, we also propose an intensified QMKRSL using a bilateral gradient technique named QMKRSL_BG, in an effort to further improve filtering accuracy. Short-term chaotic time-series prediction experiments are conducted to demonstrate the satisfactory performance of our algorithms.

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Self-organizing kernel adaptive filtering

Cited by 14 publications

References 44 publications

Online Distributed Learning Over Networks in RKH Spaces Using Random Fourier Features

Online Distributed Learning Over Networks in RKH Spaces Using Random Fourier Features

Improving sparsity in kernel adaptive filters using a unit-norm dictionary

A Quantized Kernel Learning Algorithm Using a Minimum Kernel Risk-Sensitive Loss Criterion and Bilateral Gradient Technique

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