Ting-Zhu Huang mainly investigates Algorithm, Artificial intelligence, Combinatorics, Mathematical optimization and Multi-agent system. His Algorithm research is multidisciplinary, relying on both Hilbert space, Numerical analysis and Heaviside step function. His research integrates issues of Rate of convergence, Computer vision and Pattern recognition in his study of Artificial intelligence.
Ting-Zhu Huang has included themes like Hadamard's inequality, Hadamard matrix, Pure mathematics, Upper and lower bounds and Eigenvalues and eigenvectors in his Combinatorics study. His Mathematical optimization research includes themes of Image resolution, Total variation denoising and Image restoration. His work investigates the relationship between Linear system and topics such as Complex system that intersect with problems in Applied mathematics.
Ting-Zhu Huang spends much of his time researching Algorithm, Applied mathematics, Linear system, Matrix and Mathematical analysis. Ting-Zhu Huang studies Regularization which is a part of Algorithm. His study in Applied mathematics is interdisciplinary in nature, drawing from both Iterative method, Krylov subspace, Mathematical optimization, System of linear equations and Discretization.
The various areas that Ting-Zhu Huang examines in his Iterative method study include Numerical analysis, Toeplitz matrix and Coefficient matrix. His Linear system study integrates concerns from other disciplines, such as Rate of convergence and Residual. His research investigates the link between Matrix and topics such as Combinatorics that cross with problems in Eigenvalues and eigenvectors, Upper and lower bounds, Diagonally dominant matrix and Spectral radius.
The scientist’s investigation covers issues in Algorithm, Applied mathematics, Rank, Regularization and Artificial intelligence. His Algorithm research integrates issues from Tucker decomposition, Hyperspectral imaging, Matrix decomposition, Norm and Image restoration. He has researched Applied mathematics in several fields, including Krylov subspace, Residual, Generalized minimal residual method, Discretization and Discontinuous Galerkin method.
His study looks at the intersection of Generalized minimal residual method and topics like Eigenvalues and eigenvectors with Linear system, Saddle point and Iterative method. His study in the field of Total variation denoising also crosses realms of Ultrasound. His Artificial intelligence research is multidisciplinary, incorporating elements of Computer vision and Pattern recognition.
His primary areas of study are Algorithm, Rank, Regularization, Artificial intelligence and Matrix decomposition. His biological study spans a wide range of topics, including Hyperspectral imaging, Streak, Tensor, Norm and Image restoration. Ting-Zhu Huang works mostly in the field of Rank, limiting it down to topics relating to Tensor and, in certain cases, Applied mathematics and Minification, as a part of the same area of interest.
His work on Total variation denoising as part of his general Regularization study is frequently connected to High order, thereby bridging the divide between different branches of science. The concepts of his Artificial intelligence study are interwoven with issues in Computer vision and Pattern recognition. The various areas that Ting-Zhu Huang examines in his Matrix decomposition study include Tucker decomposition and Tensor completion.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Deblurring and Sparse Unmixing for Hyperspectral Images
Xi-Le Zhao;Fan Wang;Ting-Zhu Huang;M. K. Ng.
IEEE Transactions on Geoscience and Remote Sensing (2013)
Simple criteria for nonsingular H-matrices
Tai-Bin Gan;Ting-Zhu Huang.
Linear Algebra and its Applications (2003)
A Novel Tensor-Based Video Rain Streaks Removal Approach via Utilizing Discriminatively Intrinsic Priors
Tai-Xiang Jiang;Ting-Zhu Huang;Xi-Le Zhao;Liang-Jian Deng.
computer vision and pattern recognition (2017)
Image restoration using total variation with overlapping group sparsity
Jun Liu;Ting-Zhu Huang;Ivan W. Selesnick;Xiao-Guang Lv.
Information Sciences (2015)
Tensor completion using total variation and low-rank matrix factorization
Teng-Yu Ji;Ting-Zhu Huang;Xi-Le Zhao;Tian-Hui Ma.
Information Sciences (2016)
FastDeRain: A Novel Video Rain Streak Removal Method Using Directional Gradient Priors
Tai-Xiang Jiang;Ting-Zhu Huang;Xi-Le Zhao;Liang-Jian Deng.
IEEE Transactions on Image Processing (2019)
An active contour model and its algorithms with local and global Gaussian distribution fitting energies
Hui Wang;Ting-Zhu Huang;Zongben Xu;Yilun Wang.
Information Sciences (2014)
Leader-following consensus for multi-agent systems via sampled-data control
Z.-J. Tang;T.-Z. Huang;J.-L. Shao;J.-P. Hu.
Iet Control Theory and Applications (2011)
Fast Iterative Method with a Second-Order Implicit Difference Scheme for Time-Space Fractional Convection–Diffusion Equation
Xian-Ming Gu;Xian-Ming Gu;Ting-Zhu Huang;Cui-Cui Ji;Bruno Carpentieri.
Journal of Scientific Computing (2017)
Fast iterative method with a second order implicit difference scheme for time-space fractional convection-diffusion equations
Xian-Ming Gu;Ting-Zhu Huang;Cui-Cui Ji;Bruno Carpentieri.
arXiv: Numerical Analysis (2016)
University of Tokyo
Xi'an Jiaotong University
Western Sydney University
University of Hong Kong
Loughborough University
Sichuan University
German Aerospace Center
University of Innsbruck
New York University
Instituto Superior Técnico
Profile was last updated on December 6th, 2021.
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