Results 141 to 150 of about 708 (167)
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Projected Kernel Recursive Maximum Correntropy
IEEE Transactions on Circuits and Systems II: Express Briefs, 2018In this brief, a different kernel recursive maximum correntropy algorithm is derived using the weighted output information, called KRMC-W. To curb the network growth, we propose a new online sparsification strategy in a feature space, named vector projection (VP) method.
Ji Zhao 0005 +2 more
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Correntropy and Linear Representation
2014The nearest neighbor (NN) classifier is the most popular method for image-based object recognition. In NN classifier, the representational capacity of an image database and the recognition rate depend on how registered samples are selected to represent object’s possible variations and also how many samples are available.
Ran He +3 more
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An adaptive kernel width update for correntropy
The 2012 International Joint Conference on Neural Networks (IJCNN), 2012Correntropy, as an adaptive criterion of Information Theoretic Learning (ITL), has been successfully used in signal processing and machine learning. How to appropriately select the kernel width of correntropy is a crucial problem in correntropy applications. Existing kernel width selection methods are not suitable enough for this problem. In this paper,
Songlin Zhao +2 more
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A Separable Maximum Correntropy Adaptive Algorithm
IEEE Transactions on Circuits and Systems II: Express Briefs, 2020In this brief, a separable maximum correntropy criterion (SMCC) algorithm is developed by exploiting the typical separability property of tensors. Utilizing the separability property, a great number savings are obtained along with accelerated learning rate and improved estimate accuracy. In the proposed SMCC, a correntropy scheme is used to construct a
Wanlu Shi, Yingsong Li 0001, Badong Chen
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Correntropy in Data Classification
2012In this chapter, the usability of the correntropy-based similarity measure in the paradigm of statistical data classification is addressed. The basic theme of the chapter is to compare the performance of the correntropic loss function with the conventional quadratic loss function.
Mujahid N. Syed +2 more
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Kernel Recursive Generalized Maximum Correntropy
IEEE Signal Processing Letters, 2017In this letter, a novel kernel adaptive algorithm, called kernel recursive generalized maximum correntropy algorithm (KRGMC), is derived in a kernel space and under the generalized maximum correntropy (GMC) criterion. The proposed kernel algorithm can effectively scale down the dynamic recursive weight coefficients influenced by the impulsive estimate ...
Ji Zhao 0005, Hongbin Zhang 0002
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State space maximum correntropy filter
Signal Processing, 2017The state space recursive least squares (SSRLS) filter is a new addition to the well-known recursive least squares (RLS) family filters, which can achieve an excellent tracking performance by overcoming some limitations of the standard RLS algorithm.
Xi Liu 0006 +3 more
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Orthogonal Maximum Correntropy Learning
2022 IEEE 32nd International Workshop on Machine Learning for Signal Processing (MLSP), 2022Mingfei Lu, Badong Chen
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A correntropy function based on coincidence detection
Pattern Recognition Letters, 2017Abstract This work presents a new generalized correlation function (correntropy) estimator based on collision entropy. Both the proposed approach and the standard correntropy estimator, published in 2006, can be regarded as coincidence counting methods, one using soft coincidence detection, whereas ours detects hard coincidences.
Jugurta Montalvão +2 more
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Sequential Maximum Correntropy Kalman Filtering
Asian Journal of Control, 2018AbstractThis paper explores a linear state estimation problem in non‐Gaussian setting and suggests a computationally simple estimator based on the maximum correntropy criterion Kalman filter (MCC‐KF). The first MCC‐KF method was developed in Joseph stabilized form. It requires two n × n and one m × m matrix inversions, where n is a dimension of unknown
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