2020 - SIAM Fellow For contributions to the theory and algorithms for low-dimensional models and their applications in computer vision and image processing.
2017 - ACM Fellow For contributions to theory and application of low-dimensional models for computer vision and pattern recognition
2013 - IEEE Fellow For contributions to computer vision and pattern recognition
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.
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
Outlier most often made with reference to Subspace topology,
Linear subspace most often made with reference to Cluster analysis. His Algorithm research also works with subjects such as
Mathematical optimization, which have a strong connection to Principal component analysis and Matrix norm,
Convex optimization that connect with fields like Low-rank approximation. His Sparse approximation research includes elements of Image resolution, Pixel and Compressed sensing.
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.
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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Robust Face Recognition via Sparse Representation
J. Wright;A.Y. Yang;A. Ganesh;S.S. Sastry.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2009)
Robust principal component analysis
Emmanuel J. Candès;Xiaodong Li;Yi Ma;John Wright.
Journal of the ACM (2011)
Image Super-Resolution Via Sparse Representation
Jianchao Yang;John Wright;Thomas S Huang;Yi Ma.
IEEE Transactions on Image Processing (2010)
Robust Recovery of Subspace Structures by Low-Rank Representation
Guangcan Liu;Zhouchen Lin;Shuicheng Yan;Ju Sun.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2013)
An Invitation to 3-D Vision: From Images to Geometric Models
Yi Ma;Stefano Soatto;Jana Koseck;S. Shankar Sastry.
RASL: Robust Alignment by Sparse and Low-Rank Decomposition for Linearly Correlated Images
Yigang Peng;A. Ganesh;J. Wright;Wenli Xu.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2012)
Sparse Representation for Computer Vision and Pattern Recognition
John Wright;Yi Ma;Julien Mairal;Guillermo Sapiro.
Proceedings of the IEEE (2010)
Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Matrices via Convex Optimization
John Wright;Arvind Ganesh;Shankar Rao;Yigang Peng.
neural information processing systems (2009)
Image super-resolution as sparse representation of raw image patches
Jianchao Yang;J. Wright;T. Huang;Yi Ma.
computer vision and pattern recognition (2008)
Generalized principal component analysis (GPCA)
R. Vidal;Yi Ma;S. Sastry.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2005)
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