Results 151 to 160 of about 705 (186)
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A Privacy-Preserving Semisupervised Algorithm Under Maximum Correntropy Criterion
IEEE Transactions on Neural Networks and Learning Systems, 2022Existing semisupervised learning approaches generally focus on the single-agent (centralized) setting, and hence, there is the risk of privacy leakage during joint data processing. At the same time, using the mean square error criterion in such approaches does not allow one to efficiently deal with problems involving non-Gaussian distribution. Thus, in
Ling Zuo +3 more
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Linear Discriminant Analysis with Maximum Correntropy Criterion
2013Linear Discriminant Analysis (LDA) is a famous supervised feature extraction method for subspace learning in computer vision and pattern recognition. In this paper, a novel method of LDA based on a new Maximum Correntropy Criterion optimization technique is proposed.
Wei Zhou, Sei-ichiro Kamata
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Performance evaluation of the maximum correntropy criterion in identification systems
2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS), 2016The System identification explores ways to obtain mathematical models of an unknown system. However, as a result from the intrinsic random nature of system or from the environment noise, it is very hard to find a perfect mathematical representation of a real system.
João P. F. Guimarães +4 more
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Robust principal curves based on maximum correntropy criterion
2013 International Conference on Machine Learning and Cybernetics, 2013Principal curves are curves which pass throught the ’middle’ of a data cloud. They are sensitive to variances of data clouds. In this paper, we propose a robust principal curve model Correntropy based Principal Curve (CPC) model, based on maximum correntropy criterion (MCC).
Chun-Guo Li, Bao-Gang Hu
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Recursive Constrained Maximum Correntropy Criterion Algorithm for Adaptive Filtering
IEEE Transactions on Circuits and Systems II: Express Briefs, 2020Recently, the gradient based constrained maximum correntropy criterion (GCMCC) algorithm has received considerable attention since it provides superior performance to the traditional methods and is robust to the non-Gaussian noise. However, the convergence of GCMCC algorithm depends on the learning rate. With a large learning rate, GCMCC converges fast
Guobing Qian, Xiaohan Ning, Shiyuan Wang
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Robust Multidimensional Scaling Using a Maximum Correntropy Criterion
IEEE Transactions on Signal Processing, 2017Multidimensional scaling (MDS) refers to a class of dimensionality reduction techniques, which represent entities as points in a low-dimensional space so that the interpoint distances approximate the initial pairwise dissimilarities between entities as closely as possible. The traditional methods for solving MDS are susceptible to outliers.
Fotios D. Mandanas, Costas Kotropoulos
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Robust Principal Component Analysis Based on Maximum Correntropy Criterion
IEEE Transactions on Image Processing, 2011Principal component analysis (PCA) minimizes the mean square error (MSE) and is sensitive to outliers. In this paper, we present a new rotational-invariant PCA based on maximum correntropy criterion (MCC). A half-quadratic optimization algorithm is adopted to compute the correntropy objective.
Ran He 0001 +3 more
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Maximum Correntropy Criterion-Based Hierarchical One-Class Classification
IEEE Transactions on Neural Networks and Learning Systems, 2021Due to the effectiveness of anomaly/outlier detection, one-class algorithms have been extensively studied in the past. The representatives include the shallow-structure methods and deep networks, such as the one-class support vector machine (OC-SVM), one-class extreme learning machine (OC-ELM), deep support vector data description (Deep SVDD), and ...
Jiuwen Cao +5 more
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Maximum correntropy criterion partial least squares
Optik, 2018Abstract Partial least squares (PLS) has been extensively used to solve problems such as infrared quantitative analysis, economic data analysis, object tracking. PLS finds a linear regression model by projecting the predicted variables and the response to a new space.
Yi Mou +4 more
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Nyström Kernel Algorithm Under Generalized Maximum Correntropy Criterion
IEEE Signal Processing Letters, 2020The kernel adaptive filters (KAFs) based on the minimum mean square error (MMSE) criterion in reproducing kernel Hilbert space (RKHS) improve the performance of linear adaptive filters but result in instability issues and large burdens of computation and memory in impulsive noises.
Tao Zhang 0183, Shiyuan Wang
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