Results 161 to 170 of about 3,111 (174)
Some of the next articles are maybe not open access.
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, Hongbin Zhang, Gang Wang
openaire +1 more source
2014
Sparse signal representation arises in application of compressed sensing and has been considered as a significant technique in computer vision and machine learning [27, 65, 154]. Based on the l 0-l 1 equivalence theory [18, 39], the solution of an l 0-minimization problem is equal to that of an l 1 minimization problem under certain conditions.
Ran He +3 more
openaire +1 more source
Sparse signal representation arises in application of compressed sensing and has been considered as a significant technique in computer vision and machine learning [27, 65, 154]. Based on the l 0-l 1 equivalence theory [18, 39], the solution of an l 0-minimization problem is equal to that of an l 1 minimization problem under certain conditions.
Ran He +3 more
openaire +1 more source
Correntropy estimator for data reconciliation
Chemical Engineering Science, 2013Abstract Constructing a reliable model for process monitoring, control and optimization requires accurate data satisfying balance equations of absolute validity, such as mass and energy balances. Under normal circumstances, process data are inaccurate since they are affected by random errors and possibly gross errors.
Junghui Chen +2 more
openaire +1 more source
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
openaire +1 more source
Robust 2DLDA based on correntropy
Neurocomputing, 2018Abstract To further improve the robustness of two-dimensional LDA (2DLDA) methods against outliers, this paper proposes a new robust 2DLDA version which obtains the optimal projection transformation by maximizing the correntropy-based within-class similarity and maintaining the global dispersity simultaneously.
Fujin Zhong, Li Liu, Jun Hu
openaire +1 more source
Regularized correntropy criterion based semi-supervised ELM
Neural Networks, 2020Along with the explosive growing of data, semi-supervised learning attracts increasing attention in the past years due to its powerful capability in labeling unlabeled data and knowledge mining. As an emerging method, the semi-supervised extreme learning machine (SSELM), that builds on ELM, has been developed for data classification and shown ...
Yang, Jie +4 more
openaire +2 more sources
Correntropy based self-organizing map
2016 International Conference on Machine Learning and Cybernetics (ICMLC), 2016Self-organizing map (SOM) is regarded as a type of feedfoward neural network. It has been successfully used for unsupervised learning. However, the objective function of the traditional SOM relies on the mean squared error (MSE) criterion, which makes the performance of SOM become poor in the presence of noise.
Qing-Zhen Shang, Hong-Jie Xing
openaire +1 more source
Cyclostationary correntropy: Definition and applications
Expert Systems with Applications, 2017Abstract Information extraction is a frequent and relevant problem in digital signal processing. In the past few years, different methods have been utilized for the parameterization of signals and the achievement of efficient descriptors. When the signals possess statistical cyclostationary properties, the Cyclic Autocorrelation Function (CAF) and ...
Aluisio I.R. Fontes +4 more
openaire +1 more source
Orthogonal Maximum Correntropy Learning
2022 IEEE 32nd International Workshop on Machine Learning for Signal Processing (MLSP), 2022Mingfei Lu, Badong Chen
openaire +1 more source
Maximum Correntropy Two-Filter Smoothing
2023 26th International Conference on Information Fusion (FUSION), 2023Yanbo Yang +4 more
openaire +1 more source