Yiu-ming Cheung
Hong Kong Baptist University
341 Papers
1.6K Citations
Yiu-ming Cheung is an academic researcher from Hong Kong Baptist University. The author has contributed to research in topics: Computer science & Cluster analysis. The author has an hindex of 37, co-authored 300 publications. Previous affiliations of Yiu-ming Cheung include The Chinese University of Hong Kong & Huazhong University of Science and Technology.
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Papers
A Novel Evolutionary Multi-objective Algorithm Based on S Metric Selection and M2M Population Decomposition
Lei Chen,Hai-Lin Liu,Chuan Lu,Chuan Lu,Yiu-ming Cheung,Jun Zhang +5 more
- 01 Jan 2015
TL;DR: A novel S metric selection evolutionary algorithm based on the population decomposition strategy MOEA/D-M2M is proposed to give a simple but effective method to improve the effectiveness of SMS based algorithm.
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Fast Semantic Preserving Hashing for Large-Scale Cross-Modal Retrieval
Xingzhi Wang,Xin Liu,Shu-Juan Peng,Yiu-ming Cheung,Zhikai Hu,Nannan Wang +5 more
- 01 Nov 2019
TL;DR: This work first formulate the learning of similarity-preserving hash codes in terms of orthogonally rotating the semantic data to hamming space, and then proposes a novel Fast Semantic Preserving Hashing (FSePH) approach to large-scale cross-modal retrieval.
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A Divide-and-Conquer Learning Approach to Radial Basis Function Networks
Yiu-ming Cheung,Rong-Bo Huang +1 more
TL;DR: This paper presents a new divide-and-conquer based learning approach to radial basis function (RBF) networks, in which a conventional RBF network is divided into several RBF sub-networks, and finds that the performance generally varies with the different decompositions of the network’s input and the hidden layer.
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Supervised Manifold Learning for Media Interestingness Prediction.
Yang Liu,Zhonglei Gu,Yiu-ming Cheung +2 more
- 01 Jan 2016
TL;DR: The models designed for automatically selecting multimedia data, e.g., image and video segments, which are considered to be interesting for a common viewer are described, and a new dimensionality reduction method dubbed Supervised Manifold Regression is introduced to learn the compact representations for predicting the continuous interestingness levels.
•Proceedings Article
Bilinear Probabilistic Canonical Correlation Analysis via Hybrid Concatenations
Yang Zhou,Haiping Lu,Yiu-ming Cheung +2 more
- 13 Feb 2017
TL;DR: BPCCA is proposed, a new bilinear extension of PCCA, by introducing a hybrid joint model that preserves matrix structures indirectly via hybrid vector-based and matrix-based concatenations and enables BPCCA to gain more model flexibility in capturing two-view correlations and obtain close-form solutions in parameter estimation.
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