Jian-Feng Cai

What is he best known for?

The fields of study he is best known for:

• Mathematical analysis
• Artificial intelligence
• Algebra

His primary areas of investigation include Algorithm, Image restoration, Artificial intelligence, Computer vision and Deblurring. His Algorithm research is multidisciplinary, incorporating elements of Singular value, Combinatorics and Minification. His study in Image restoration is interdisciplinary in nature, drawing from both Wavelet and Bregman method.

His study focuses on the intersection of Artificial intelligence and fields such as Pattern recognition with connections in the field of Four-Dimensional Computed Tomography, Dimension and Matrix. The study incorporates disciplines such as Pixel, Impulse noise, Real image and Outlier in addition to Deblurring. His work carried out in the field of Sparse approximation brings together such families of science as Sparse matrix, Noise reduction and Tight frame.

His most cited work include:

• A Singular Value Thresholding Algorithm for Matrix Completion (3961 citations)
• Split Bregman Methods and Frame Based Image Restoration (536 citations)
• A framelet-based image inpainting algorithm (272 citations)

What are the main themes of his work throughout his whole career to date?

Jian-Feng Cai mainly investigates Algorithm, Rank, Compressed sensing, Artificial intelligence and Matrix. His research in Algorithm intersects with topics in Hankel matrix, Mathematical optimization, Minification and Projection. His Rank research incorporates elements of Subspace topology, Sampling, Combinatorics, Thresholding and Gradient descent.

His Artificial intelligence research is multidisciplinary, incorporating perspectives in Computer vision and Pattern recognition. Low-rank approximation, Matrix completion, Sparse matrix, Robust principal component analysis and Matrix decomposition are among the areas of Matrix where the researcher is concentrating his efforts. While the research belongs to areas of Matrix norm, he spends his time largely on the problem of Singular value, intersecting his research to questions surrounding Interior point method.

He most often published in these fields:

• Algorithm (45.24%)
• Rank (24.60%)
• Compressed sensing (23.02%)

What were the highlights of his more recent work (between 2018-2021)?

• Algorithm (45.24%)
• Rank (24.60%)
• Regularization (11.11%)

In recent papers he was focusing on the following fields of study:

Jian-Feng Cai mostly deals with Algorithm, Rank, Regularization, Hankel matrix and Combinatorics. His studies in Algorithm integrate themes in fields like Matrix, Noise reduction, Projection and Feature. His work deals with themes such as Gradient descent, Subspace topology and Outlier, which intersect with Rank.

The Regularization study combines topics in areas such as Data-driven and Compressed sensing. Jian-Feng Cai combines subjects such as Manifold, Quadratic equation, Order and Restricted isometry property with his study of Combinatorics. In his research on the topic of Singular value decomposition, Image restoration, Wavelet, Toeplitz matrix and Piecewise is strongly related with Low-rank approximation.

Between 2018 and 2021, his most popular works were:

• Fast and Provable Algorithms for Spectrally Sparse Signal Reconstruction via Low-Rank Hankel Matrix Completion (35 citations)
• Accelerated Alternating Projections for Robust Principal Component Analysis (16 citations)
• Fast Single Image Reflection Suppression via Convex Optimization (11 citations)

In his most recent research, the most cited papers focused on:

• Mathematical analysis
• Artificial intelligence
• Algebra

Jian-Feng Cai spends much of his time researching Rank, Hankel matrix, Subspace topology, Combinatorics and Robustness. Rank is frequently linked to Thresholding in his study. His studies deal with areas such as Time domain, Algorithm, Signal reconstruction and Order as well as Hankel matrix.

His research in Subspace topology tackles topics such as Sparse matrix which are related to areas like Rate of convergence, Robust principal component analysis, Singular value decomposition and Low-rank approximation. His Combinatorics study combines topics from a wide range of disciplines, such as Function and Quadratic equation. His Robustness research includes themes of Projection method, Discrete mathematics, Minification, Space and Computation.