What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His primary areas of study are Mathematical optimization, Artificial intelligence, Machine learning, Regularization and Lasso.
Jun Liu has included themes like Convex relaxation, Algorithm, Feature selection and Benchmark data in his Mathematical optimization study.
In his research, Curse of dimensionality is intimately related to Pattern recognition, which falls under the overarching field of Artificial intelligence.
His Machine learning research incorporates elements of Mini–Mental State Examination and Neuroimaging.
In Regularization, he works on issues like Convex optimization, which are connected to Time complexity.
His Lasso course of study focuses on Key and Computational complexity theory and Subgradient method.
His most cited work include:
- Multi-task feature learning via efficient l 2, 1 -norm minimization (456 citations)
- SLEP: Sparse Learning with Efficient Projections (418 citations)
- Face liveness detection from a single image with sparse low rank bilinear discriminative model (345 citations)
What are the main themes of his work throughout his whole career to date?
Jun Liu mainly focuses on Artificial intelligence, Algorithm, Mathematical optimization, Regularization and Pattern recognition.
His Artificial intelligence study incorporates themes from Machine learning, Neuroimaging and Computer vision.
While the research belongs to areas of Algorithm, he spends his time largely on the problem of Image, intersecting his research to questions surrounding Wavelet transform.
The concepts of his Mathematical optimization study are interwoven with issues in Gradient descent, Lasso and Feature selection.
His Regularization study combines topics from a wide range of disciplines, such as Smoothness, Optimization problem and Data mining.
His work on Classifier and Discriminative model as part of general Pattern recognition research is often related to Set, thus linking different fields of science.
He most often published in these fields:
- Artificial intelligence (44.09%)
- Algorithm (27.96%)
- Mathematical optimization (25.81%)
What were the highlights of his more recent work (between 2015-2021)?
- Algorithm (27.96%)
- Artificial intelligence (44.09%)
- Machine learning (15.05%)
In recent papers he was focusing on the following fields of study:
Jun Liu mostly deals with Algorithm, Artificial intelligence, Machine learning, Coronavirus disease 2019 and Value.
His Algorithm study focuses on Optimization problem in particular.
His study in Artificial intelligence concentrates on Medical imaging, Deep learning, Tree kernel, Kernel method and Polynomial kernel.
His Lasso research integrates issues from Efficient algorithm, Convex function and Mathematical optimization.
Jun Liu interconnects Regularization and Coordinate descent in the investigation of issues within Almost surely.
His Regularization study combines topics from a wide range of disciplines, such as Smoothness, Neuroimaging and Regression.
Between 2015 and 2021, his most popular works were:
- Temporally Constrained Group Sparse Learning for Longitudinal Data Analysis in Alzheimer's Disease (35 citations)
- Greedy Orthogonal Pivoting Algorithm for Non-Negative Matrix Factorization (6 citations)
- Absolute Fused Lasso and Its Application to Genome-Wide Association Studies (6 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Jun Liu mainly investigates Artificial intelligence, Combinatorics, Non-negative matrix factorization, Efficient algorithm and Mathematical optimization.
While working in this field, Jun Liu studies both Artificial intelligence and Photoacoustic imaging in biomedicine.
His research integrates issues of Convex function, Lasso and Non convex optimization in his study of Efficient algorithm.
His study connects Algorithm and Mathematical optimization.
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