What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Yiu-ming Cheung focuses on Artificial intelligence, Pattern recognition, Cluster analysis, Computer vision and Robustness.
As part of his studies on Artificial intelligence, Yiu-ming Cheung often connects relevant areas like Machine learning.
His Cluster analysis study incorporates themes from Kernel embedding of distributions, Kernel method and Tree kernel.
His study in the field of Wavelet, Feature, Image and Frequency domain also crosses realms of Clutter.
His studies deal with areas such as Subspace topology and Optimization problem, Mathematical optimization as well as Robustness.
His work carried out in the field of Correlation clustering brings together such families of science as Radial basis function kernel and Variable kernel density estimation.
His most cited work include:
- Segmentation of retinal blood vessels using the radial projection and semi-supervised approach (266 citations)
- Robust Object Tracking via Key Patch Sparse Representation (180 citations)
- Agglomerative Fuzzy K-Means Clustering Algorithm with Selection of Number of Clusters (169 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Cluster analysis, Algorithm and Computer vision.
His work deals with themes such as Machine learning and Data mining, which intersect with Artificial intelligence.
His Pattern recognition research is multidisciplinary, incorporating elements of Pixel, Facial recognition system, Feature and Subspace topology.
His Cluster analysis research incorporates themes from Selection, Model selection and Expectation–maximization algorithm.
The concepts of his Algorithm study are interwoven with issues in Theoretical computer science, Artificial neural network, Independent component analysis and Evolutionary algorithm, Mathematical optimization.
Yiu-ming Cheung is interested in Wavelet, which is a field of Computer vision.
He most often published in these fields:
- Artificial intelligence (65.15%)
- Pattern recognition (39.09%)
- Cluster analysis (20.20%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (65.15%)
- Pattern recognition (39.09%)
- Cluster analysis (20.20%)
In recent papers he was focusing on the following fields of study:
Yiu-ming Cheung mainly investigates Artificial intelligence, Pattern recognition, Cluster analysis, Algorithm and Image.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and Computer vision.
The Feature extraction research Yiu-ming Cheung does as part of his general Pattern recognition study is frequently linked to other disciplines of science, such as HSL and HSV, therefore creating a link between diverse domains of science.
His study in Cluster analysis is interdisciplinary in nature, drawing from both Metric, Time complexity, Measure, Categorical variable and Ordinal number.
His Algorithm study incorporates themes from Subspace topology, Matrix norm, Tensor, Rank and Principal component analysis.
His work carried out in the field of Image brings together such families of science as Preprocessor and Feature.
Between 2018 and 2021, his most popular works were:
- Feature Extraction for Incomplete Data Via Low-Rank Tensor Decomposition With Feature Regularization (21 citations)
- A high-capacity reversible data hiding method for homomorphic encrypted images (20 citations)
- Robust heterogeneous discriminative analysis for face recognition with single sample per person (18 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Cluster analysis, Facial recognition system and Feature extraction.
His Artificial intelligence research incorporates elements of Optimization problem and Machine learning, Particle swarm optimization.
The study incorporates disciplines such as Information hiding, Image, Contextual image classification, Pixel and Sparse matrix in addition to Pattern recognition.
His Cluster analysis research integrates issues from Matrix decomposition, Structure, Time complexity and Data set.
Yiu-ming Cheung combines subjects such as Data modeling, Discriminative model, Missing data and Biometrics with his study of Facial recognition system.
Yiu-ming Cheung has researched Feature extraction in several fields, including Representation, DUAL, Structure, Manifold and Linear programming.
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