H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 64 Citations 18,614 264 World Ranking 1204 National Ranking 112

Research.com Recognitions

Awards & Achievements

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

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Algorithm
  • 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 most cited work include:

  • Robust Recovery of Subspace Structures by Low-Rank Representation (2079 citations)
  • Robust Subspace Segmentation by Low-Rank Representation (1144 citations)
  • Linearized Alternating Direction Method with Adaptive Penalty for Low-Rank Representation (611 citations)

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

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.

He most often published in these fields:

  • Artificial intelligence (53.77%)
  • Pattern recognition (28.05%)
  • Algorithm (24.68%)

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

  • Artificial intelligence (53.77%)
  • Algorithm (24.68%)
  • Pattern recognition (28.05%)

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

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

  • Tensor which is related to area like Rank, Matrix norm and Tensor,
  • Cluster analysis, which have a strong connection to Robustness.

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.

Between 2018 and 2021, his most popular works were:

  • Tensor Robust Principal Component Analysis with a New Tensor Nuclear Norm (152 citations)
  • Subspace Clustering by Block Diagonal Representation (105 citations)
  • Expectation-Maximization Attention Networks for Semantic Segmentation (73 citations)

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

  • Artificial intelligence
  • Algorithm
  • Machine learning

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.

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.

Top Publications

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)

2162 Citations

Robust Subspace Segmentation by Low-Rank Representation

Guangcan Liu;Zhouchen Lin;Yong Yu.
international conference on machine learning (2010)

1548 Citations

Linearized Alternating Direction Method with Adaptive Penalty for Low-Rank Representation

Zhouchen Lin;Risheng Liu;Zhixun Su.
neural information processing systems (2011)

735 Citations

Fundamental limits of reconstruction-based superresolution algorithms under local translation

Zhouchen Lin;Heung-Yeung Shum.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2004)

570 Citations

Fast Convex Optimization Algorithms for Exact Recovery of a Corrupted Low-Rank Matrix

Zhouchen Lin;Arvind Ganesh;John Wright;Leqin Wu.
(2009)

522 Citations

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)

400 Citations

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)

367 Citations

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)

305 Citations

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)

303 Citations

Form factor and input method for language input

Zhouchen Lin;Rongrong Wang;Jian Wang.
(2005)

271 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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