Multikernel Adaptive Filters Under the Minimum Cauchy Kernel Loss Criterion

Shi, Wei; Xiong, Kui; Wang, Shiyuan

doi:10.1109/access.2019.2936973

Cited by 13 publications

(4 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nevertheless, it has been shown in recent times that nonconvex loss functions improve the generic applicability and robustness of learning, especially in situations where the data and noise distributions are unknown [ 42 ]. One such loss function is given in Equation (10) as a Cauchy kernel risk-sensitive loss (CKRSL), derived using a Gaussian kernel-adapted operator and following methods similar to those in [ 43 , 44 ].

…”

Section: Methodsmentioning

confidence: 99%

Toward Precision Radiotherapy: A Nonlinear Optimization Framework and an Accelerated Machine Learning Algorithm for the Deconvolution of Tumor-Infiltrating Immune Cells

Okereke

Bello

Onwukwe

2022

Cells

View full text Add to dashboard Cite

Tumor-infiltrating immune cells (TIICs) form a critical part of the ecosystem surrounding a cancerous tumor. Recent advances in radiobiology have shown that, in addition to damaging cancerous cells, radiotherapy drives the upregulation of immunosuppressive and immunostimulatory TIICs, which in turn impacts treatment response. Quantifying TIICs in tumor samples could form an important predictive biomarker guiding patient stratification and the design of radiotherapy regimens and combined immune-radiation treatments. As a result of several limitations associated with experimental methods for quantifying TIICs and the availability of extensive gene sequencing data, deconvolution-based computational methods have appeared as a suitable alternative for quantifying TIICs. Accordingly, we introduce and discuss a nonlinear regression approach (remarkably different from the traditional linear modeling approach of current deconvolution-based methods) and a machine learning algorithm for approximating the solution of the resulting constrained optimization problem. This way, the deconvolution problem is treated naturally, given that the gene expression levels of pure and heterogenous samples do not have a strictly linear relationship. When applied across transcriptomics datasets, our approach, which also allows the coupling of different loss functions, yields results that closely match ground-truth values from experimental methods and exhibits superior performance over popular deconvolution-based methods.

show abstract

…”

Section: Methodsmentioning

confidence: 99%

Toward Precision Radiotherapy: A Nonlinear Optimization Framework and an Accelerated Machine Learning Algorithm for the Deconvolution of Tumor-Infiltrating Immune Cells

Okereke

Bello

Onwukwe

2022

Cells

View full text Add to dashboard Cite

show abstract

“…According to (12), the estimated output is given byf (u) = ( z ) T z(u) with z being the weight vector in the feature space. Thus, the proposed Nyström mapping based on k-means sampling can be applied in nonlinear adaptive filtering based on different cost functions, e.g., the correntropy [32], Cauchy kernel loss [33], and hyperbolic cosine loss [34], for online applications in the transformed feature space.…”

Section: Nyström Kernel Mappingmentioning

confidence: 99%

The Nyström Kernel Conjugate Gradient Algorithm Based on $k$ -Means Sampling

Xiong

Wang

2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

The kernel conjugate gradient (KCG) algorithms have been proposed to improve the convergence rate and the filtering accuracy of kernel adaptive filters (KAFs) efficiently. However, sparsification is necessary in the KCG algorithms to curb the growth of network structure for online applications. To this end, a novel online kernel conjugate gradient algorithm under the mean square error criterion is proposed to approximate the kernel matrix in KAFs by combining k-means sampling into the Nyström method in a fixed-dimensional feature space, namely a Nyström kernel conjugate gradient algorithm based on k-means sampling (NysKCG-KM). The approximation accuracy of the kernel matrix as well as the filtering performance of NysKCG-KM are therefore guaranteed by k-means sampling. The proposed NysKCG-KM with no requirement of sparsification can achieve dramatically better filtering accuracy than the kernel least mean square with sparsification, and approach the filtering accuracy of the kernel recursive least squares with sparsification. Monte Carlo simulations using both the synthetic and real-world data validate the superiorities of the proposed NysKCG-KM.

show abstract

“…In [26], the quantized minimum kernel risk-sensitive loss (QMKRSL) algorithm was proposed to achieve better and robust performance of nonlinear filtering for outliers. Motivated by the studies in [27,28] on the Cauchy loss which has been successfully used in various robust learning applications, the multikernel minimum Cauchy kernel loss (MKMCKL) algorithm was reported in [29] showing the improved nonlinear filtering performance over counterpart single algorithm in the presence of extreme outliers. Recently, the kernel affine projection-like (KAPL) algorithm in RKHS was proposed and investigated for nonlinear channel equalization in scenarios of non-Gaussian noises [30].…”

Section: Introductionmentioning

confidence: 99%

Kernel Least Logarithmic Absolute Difference Algorithm

Wei

Shi

et al. 2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Kernel adaptive filtering (KAF) algorithms derived from the second moment of error criterion perform very well in nonlinear system identification under assumption of the Gaussian observation noise; however, they inevitably suffer from severe performance degradation in the presence of non-Gaussian impulsive noise and interference. To resolve this dilemma, we propose a novel robust kernel least logarithmic absolute difference (KLLAD) algorithm based on logarithmic error cost function in reproducing kernel Hilbert spaces, taking into account of the non-Gaussian impulsive noise. The KLLAD algorithm shows considerable improvement over the existing KAF algorithms without restraining impulsive interference in terms of robustness and convergence speed. Moreover, the convergence condition of KLLAD algorithm with Gaussian kernel and fixed dictionary is presented in the mean sense. The superior performance of KLLAD algorithm is confirmed by the simulation results.

show abstract

Multikernel Adaptive Filters Under the Minimum Cauchy Kernel Loss Criterion

Cited by 13 publications

References 47 publications

Toward Precision Radiotherapy: A Nonlinear Optimization Framework and an Accelerated Machine Learning Algorithm for the Deconvolution of Tumor-Infiltrating Immune Cells

Toward Precision Radiotherapy: A Nonlinear Optimization Framework and an Accelerated Machine Learning Algorithm for the Deconvolution of Tumor-Infiltrating Immune Cells

The Nyström Kernel Conjugate Gradient Algorithm Based on $k$ -Means Sampling

Kernel Least Logarithmic Absolute Difference Algorithm

Contact Info

Product

Resources

About