What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Statistics
His primary areas of study are Artificial intelligence, Computer vision, Pattern recognition, Sparse approximation and Convex optimization.
His Image segmentation, Facial recognition system, Robustness, Image processing and Feature extraction study are his primary interests in Artificial intelligence.
In his research on the topic of Computer vision, Information extraction is strongly related with Algorithm design.
His Pattern recognition research is multidisciplinary, incorporating perspectives in Data modeling, Subspace topology, Cluster analysis, Data point and Benchmark.
His biological study spans a wide range of topics, including Signal processing and Compressed sensing.
His Convex optimization research is multidisciplinary, relying on both Mathematical optimization, Algorithm and Matrix, Sparse matrix.
His most cited work include:
- Robust Face Recognition via Sparse Representation (7978 citations)
- Robust principal component analysis (4697 citations)
- Image Super-Resolution Via Sparse Representation (3620 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Sparse approximation are his primary areas of study.
His work is connected to Facial recognition system, Image segmentation, Robustness, Segmentation and Feature extraction, as a part of Artificial intelligence.
His work is dedicated to discovering how Computer vision, Invariant are connected with Planar and other disciplines.
His Pattern recognition study also includes
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Outlier most often made with reference to Subspace topology,
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Linear subspace most often made with reference to Cluster analysis.
His Algorithm research also works with subjects such as
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Mathematical optimization, which have a strong connection to Principal component analysis and Matrix norm,
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Convex optimization that connect with fields like Low-rank approximation.
His Sparse approximation research includes elements of Image resolution, Pixel and Compressed sensing.
He most often published in these fields:
- Artificial intelligence (63.60%)
- Computer vision (36.04%)
- Pattern recognition (29.68%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (63.60%)
- Algorithm (27.56%)
- Convolutional neural network (3.89%)
In recent papers he was focusing on the following fields of study:
Yi Ma spends much of his time researching Artificial intelligence, Algorithm, Convolutional neural network, Pattern recognition and Computer vision.
The Artificial intelligence study combines topics in areas such as Machine learning and Code.
His Algorithm research incorporates elements of Gradient descent, Matrix, Robustness and Rank.
The study incorporates disciplines such as Vanishing point, Feature and Convolution in addition to Convolutional neural network.
His work investigates the relationship between Pattern recognition and topics such as Normalization that intersect with problems in Normalization.
Yi Ma interconnects Representation and Cuboid in the investigation of issues within Computer vision.
Between 2016 and 2021, his most popular works were:
- Robust Low-Rank Tensor Recovery with Rectification and Alignment (71 citations)
- Structured Attentions for Visual Question Answering (68 citations)
- Learning to Parse Wireframes in Images of Man-Made Environments (63 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Statistics
His primary areas of investigation include Artificial intelligence, Function, Pattern recognition, Artificial neural network and Algorithm.
His work deals with themes such as Machine learning and Code, which intersect with Artificial intelligence.
His Pattern recognition research incorporates themes from Invariant and Cluster analysis.
Yi Ma focuses mostly in the field of Algorithm, narrowing it down to matters related to Tensor and, in some cases, Rank, Sparse matrix, Curse of dimensionality and Outlier.
His research brings together the fields of Computer vision and Convolutional neural network.
His Computer vision study combines topics in areas such as Salient and Representation.
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