H
Hong Zeng
Researcher at Southeast University
Publications - 52
Citations - 1058
Hong Zeng is an academic researcher from Southeast University. The author has contributed to research in topics: Cluster analysis & Correlation clustering. The author has an hindex of 13, co-authored 52 publications receiving 781 citations. Previous affiliations of Hong Zeng include Hong Kong Baptist University & Nanjing University of Information Science and Technology.
Papers
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Feature Selection and Kernel Learning for Local Learning-Based Clustering
Hong Zeng,Yiu-ming Cheung +1 more
TL;DR: The aim of this paper is to obtain an appropriate data representation through feature selection or kernel learning within the framework of the Local Learning-Based Clustering (LLC) method, which can outperform the global learning-based ones when dealing with the high-dimensional data lying on manifold.
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Wavelet Transform Time-Frequency Image and Convolutional Network-Based Motor Imagery EEG Classification
Baoguo Xu,Zhang Linlin,Aiguo Song,Wu Changcheng,Li Wenlong,Zhang Dalin,Guozheng Xu,Huijun Li,Hong Zeng +8 more
TL;DR: The results show that the method using convolutional neural network can be comparable or better than the other state-of-the-art approaches, and the performance will be improved when there is sufficient data.
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Semi-Supervised Maximum Margin Clustering with Pairwise Constraints
Hong Zeng,Yiu-ming Cheung +1 more
TL;DR: This paper proposes a pairwise constrained MMC algorithm and proposes a set of effective loss functions for discouraging the violation of given pairwise constraints, and presents an efficient subgradient projection optimization method to solve each convex problem in the CCCP sequence.
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A new feature selection method for Gaussian mixture clustering
Hong Zeng,Yiu-ming Cheung +1 more
TL;DR: This paper proposes a new feature selection method, through which not only the most relevant features are identified, but the redundant features are also eliminated so that the smallest relevant feature subset can be found.
Journal ArticleDOI
Discriminative spatial-frequency-temporal feature extraction and classification of motor imagery EEG: An sparse regression and Weighted Naïve Bayesian Classifier-based approach
TL;DR: Experimental results on two public EEG datasets demonstrate that optimizing spatial-frequency-temporal patterns in a data-driven manner for discriminative feature extraction greatly improves the classification performance.