What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Qibin Zhao spends much of his time researching Artificial intelligence, Feature extraction, Pattern recognition, Matrix decomposition and Multilinear map.
Qibin Zhao is interested in Overfitting, which is a field of Artificial intelligence.
Qibin Zhao has included themes like Rehabilitation, Neurophysiology, Brain–computer interface, Signal processing and Wavelet transform in his Feature extraction study.
Pattern recognition is closely attributed to Speech recognition in his work.
His Matrix decomposition study incorporates themes from Tucker decomposition, Polynomial, Blind signal separation and Data analysis.
His study in Multilinear map is interdisciplinary in nature, drawing from both Singular value decomposition, Mathematical optimization and Rank.
His most cited work include:
- Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis (742 citations)
- Tensor Decompositions for Signal Processing Applications From Two-way to Multiway Component Analysis (327 citations)
- Bayesian CP Factorization of Incomplete Tensors with Automatic Rank Determination (249 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Algorithm, Brain–computer interface and Tensor.
His Artificial intelligence research includes elements of Multilinear map, Machine learning and Electroencephalography.
His studies deal with areas such as Subspace topology and Noise reduction as well as Pattern recognition.
His Algorithm research is multidisciplinary, incorporating perspectives in Ring, Matrix decomposition, Matrix and Rank.
His studies in Brain–computer interface integrate themes in fields like Cognitive psychology, Component analysis, Oddball paradigm and Human–computer interaction.
His biological study spans a wide range of topics, including Tucker decomposition, Wavelet transform and Feature.
He most often published in these fields:
- Artificial intelligence (57.24%)
- Pattern recognition (44.74%)
- Algorithm (23.03%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (57.24%)
- Pattern recognition (44.74%)
- Tensor (20.39%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Pattern recognition, Tensor, Algorithm and Feature extraction.
The study of Artificial intelligence is intertwined with the study of Outer product in a number of ways.
His Pattern recognition study incorporates themes from Artificial neural network, Multilinear map, Noise reduction and Image restoration.
Qibin Zhao has included themes like Iterative reconstruction and Tensor product in his Tensor study.
Qibin Zhao works mostly in the field of Algorithm, limiting it down to topics relating to Matrix and, in certain cases, Ring, Unsupervised learning and Limit, as a part of the same area of interest.
His work in Feature extraction covers topics such as Feature which are related to areas like Autoencoder, Wavelet transform and Electroencephalography.
Between 2019 and 2021, his most popular works were:
- Topological Network Analysis of Early Alzheimer’s Disease Based on Resting-State EEG (6 citations)
- Robust Tensor Decomposition via Orientation Invariant Tubal Nuclear Norms (4 citations)
- Rank minimization on tensor ring: an efficient approach for tensor decomposition and completion (4 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Qibin Zhao mainly investigates Artificial intelligence, Pattern recognition, Matrix norm, Tensor decomposition and Applied mathematics.
Artificial intelligence is closely attributed to Tensor in his research.
His Pattern recognition research includes themes of Manifold, Image, Perspective, Convolution and Iterative reconstruction.
His Matrix norm research focuses on Factorization and how it relates to Hyperspectral imaging.
His Applied mathematics research incorporates elements of Multilinear map and Overfitting.
The various areas that Qibin Zhao examines in his Noise reduction study include Feature, Feature extraction, Wavelet transform and Recurrent neural network.
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