Yong Xu focuses on Artificial intelligence, Pattern recognition, Facial recognition system, Feature extraction and Sparse approximation. He has included themes like Algorithm, Regression analysis, Data mining and Computer vision in his Artificial intelligence study. His Pattern recognition study frequently draws parallels with other fields, such as Contextual image classification.
His work investigates the relationship between Facial recognition system and topics such as k-nearest neighbors algorithm that intersect with problems in Nearest neighbor search. His studies in Feature extraction integrate themes in fields like Biometrics, Subspace topology and Nonlinear dimensionality reduction, Dimensionality reduction. His biological study spans a wide range of topics, including Pixel, Rate of convergence, Sparse matrix and Robust regression.
His main research concerns Artificial intelligence, Pattern recognition, Feature extraction, Facial recognition system and Computer vision. His study in Discriminative model, Pattern recognition, Face, Biometrics and Image are all subfields of Artificial intelligence. His Pattern recognition research incorporates elements of Pixel and Feature.
His Feature extraction study combines topics from a wide range of disciplines, such as Subspace topology, Data mining, Projection, Feature vector and Dimensionality reduction. His Facial recognition system research is multidisciplinary, relying on both Algorithm, Sample, Representation and k-nearest neighbors algorithm. His Linear discriminant analysis research is multidisciplinary, incorporating perspectives in Kernel Fisher discriminant analysis and Discriminant.
Yong Xu spends much of his time researching Artificial intelligence, Pattern recognition, Convolutional neural network, Image and Block. His work is connected to Feature extraction, Deep learning, Feature, Cluster analysis and Convolution, as a part of Artificial intelligence. His research investigates the connection between Pattern recognition and topics such as Contextual image classification that intersect with issues in Sparse approximation.
His Convolutional neural network study integrates concerns from other disciplines, such as Noise, Noise reduction and Code. His study looks at the relationship between Image and fields such as Algorithm, as well as how they intersect with chemical problems. His work deals with themes such as Discriminant and Feature learning, which intersect with Discriminative model.
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Convolutional neural network, Image and Feature extraction. His studies deal with areas such as Sequence and Computer vision as well as Artificial intelligence. His Image processing study, which is part of a larger body of work in Computer vision, is frequently linked to Boundary, bridging the gap between disciplines.
His Pattern recognition research incorporates themes from Matrix decomposition and Cluster analysis. His work carried out in the field of Convolutional neural network brings together such families of science as Noise reduction, Image fusion, Regression and Code. The concepts of his Feature extraction study are interwoven with issues in Filter, DUAL, Tree, Compression and Discriminative model.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
A Survey of Sparse Representation: Algorithms and Applications
Zheng Zhang;Yong Xu;Jian Yang;Xuelong Li.
IEEE Access (2015)
A Two-Phase Test Sample Sparse Representation Method for Use With Face Recognition
Yong Xu;D. Zhang;Jian Yang;Jing-Yu Yang.
IEEE Transactions on Circuits and Systems for Video Technology (2011)
Image retrieval based on micro-structure descriptor
Guang-Hai Liu;Zuo-Yong Li;Lei Zhang;Yong Xu.
Pattern Recognition (2011)
Nuclear Norm Based Matrix Regression with Applications to Face Recognition with Occlusion and Illumination Changes
Jian Yang;Lei Luo;Jianjun Qian;Ying Tai.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2017)
LPP solution schemes for use with face recognition
Yong Xu;Aini Zhong;Jian Yang;David Zhang.
Pattern Recognition (2010)
Sparse Representation Classifier Steered Discriminative Projection With Applications to Face Recognition
Jian Yang;Delin Chu;Lei Zhang;Yong Xu.
IEEE Transactions on Neural Networks (2013)
Multilinear Sparse Principal Component Analysis
Zhihui Lai;Yong Xu;Qingcai Chen;Jian Yang.
IEEE Transactions on Neural Networks (2014)
Mind the Class Weight Bias: Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptation
Hongliang Yan;Yukang Ding;Peihua Li;Qilong Wang.
computer vision and pattern recognition (2017)
Beyond sparsity: The role of L1-optimizer in pattern classification
Jian Yang;Lei Zhang;Yong Xu;Jing-yu Yang.
Pattern Recognition (2012)
Discriminative Transfer Subspace Learning via Low-Rank and Sparse Representation
Yong Xu;Xiaozhao Fang;Jian Wu;Xuelong Li.
IEEE Transactions on Image Processing (2016)
Profile was last updated on December 6th, 2021.
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