2018 - IEEE Fellow For contributions to low-rank data modeling and image processing
2016 - Fellow of the International Association for Pattern Recognition (IAPR) For contributions to image processing, computer vision and machine learning
Artificial intelligence, Pattern recognition, Computer vision, Algorithm and Cluster analysis are his primary areas of study. His work carried out in the field of Artificial intelligence brings together such families of science as Machine learning and Linear subspace. His Pattern recognition research integrates issues from Subspace topology, Sparse matrix, Graph and Linear combination.
His study in Computer vision is interdisciplinary in nature, drawing from both Focus and Computer graphics. Zhouchen Lin has included themes like Representation, Mathematical optimization and Rank in his Algorithm study. His studies in Cluster analysis integrate themes in fields like Graph theory and Laplacian matrix.
His main research concerns Artificial intelligence, Pattern recognition, Algorithm, Computer vision and Mathematical optimization. The study incorporates disciplines such as Machine learning and Graph in addition to Artificial intelligence. His Pattern recognition research incorporates themes from Subspace topology, Linear subspace and Benchmark.
The Algorithm study combines topics in areas such as Representation and Minification. Many of his studies on Mathematical optimization apply to Convex optimization as well. Zhouchen Lin works mostly in the field of Singular value decomposition, limiting it down to topics relating to Rank and, in certain cases, Matrix norm, Tensor and Combinatorics.
His scientific interests lie mostly in Artificial intelligence, Algorithm, Pattern recognition, Differentiable function and Segmentation. His research integrates issues of Machine learning and Graph in his study of Artificial intelligence. His study on Algorithm also encompasses disciplines like
Zhouchen Lin connects Pattern recognition with Task analysis in his research. Zhouchen Lin combines subjects such as Pyramid, Pascal, Feature and Embedding with his study of Segmentation. His work deals with themes such as Spectral clustering, Feature learning, Linear subspace and Benchmark, which intersect with Convolutional neural network.
Zhouchen Lin spends much of his time researching Artificial intelligence, Algorithm, Pattern recognition, Segmentation and Applied mathematics. His Artificial intelligence study incorporates themes from Linear subspace and Symmetric matrix. In his research, Tensor, Regular polygon and Matrix norm is intimately related to Cluster analysis, which falls under the overarching field of Algorithm.
His studies in Pattern recognition integrate themes in fields like Embedding and Differentiable function. The various areas that he examines in his Segmentation study include Graph, Pixel, Enhanced Data Rates for GSM Evolution, Object and Pascal. His Applied mathematics research includes themes of Regularization, Acceleration, Stochastic optimization, Optimization problem and Stationary point.
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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)
Robust Subspace Segmentation by Low-Rank Representation
Guangcan Liu;Zhouchen Lin;Yong Yu.
international conference on machine learning (2010)
Linearized Alternating Direction Method with Adaptive Penalty for Low-Rank Representation
Zhouchen Lin;Risheng Liu;Zhixun Su.
neural information processing systems (2011)
Fundamental limits of reconstruction-based superresolution algorithms under local translation
Zhouchen Lin;Heung-Yeung Shum.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2004)
Fast Convex Optimization Algorithms for Exact Recovery of a Corrupted Low-Rank Matrix
Zhouchen Lin;Arvind Ganesh;John Wright;Leqin Wu.
Fast algorithms for recovering a corrupted low-rank matrix
Arvind Ganesh;Zhouchen Lin;John Wright;Leqin Wu.
ieee international workshop on computational advances in multi sensor adaptive processing (2009)
Fast, automatic and fine-grained tampered JPEG image detection via DCT coefficient analysis
Zhouchen Lin;Junfeng He;Xiaoou Tang;Chi-Keung Tang.
Pattern Recognition (2009)
Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Tensors via Convex Optimization
Canyi Lu;Jiashi Feng;Yudong Chen;Wei Liu.
computer vision and pattern recognition (2016)
Non-negative low rank and sparse graph for semi-supervised learning
Liansheng Zhuang;Haoyuan Gao;Zhouchen Lin;Yi Ma.
computer vision and pattern recognition (2012)
Form factor and input method for language input
Zhouchen Lin;Rongrong Wang;Jian Wang.
Profile was last updated on December 6th, 2021.
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