Canyi Lu

6.5k citations
33 papers · 3.8k indexed · 3 hit papers · h-index 21

Impact in

Papers in

Canyi Lu

33 papers receiving 3.7k citations

Hit Papers

Tensor Robust Principal Component Analysis with a New Tensor Nuclear Norm 2019 · 734 citations
7342016202620192022200400600

Peers

Canyi Lu
Comparison fields: 5 of 115
  • Computational Mathematics 986
  • Computer Vision and Pattern Recognition 2.3k
  • Computational Mechanics 1.7k
  • Media Technology 659
  • Signal Processing 460
Replace Yuan Xie with:
Yuan Xie China
Fanhua Shang China
Yongyong Chen China
Qian Zhao China
Qi Xie China
Xiao Fu United States
Ehsan Elhamifar United States
Zhihui Lai China
Quanxue Gao China
Lunke Fei China
Canyi Lu relative to Yuan Xie China Yuan Xie's profile →
Citations per field
00.5×2.6×
Yuan Xie · 1×
Citations per year

Countries citing papers authored by Canyi Lu

Since Specialization
Citations

This map shows the geographic impact of Canyi Lu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Canyi Lu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Canyi Lu more than expected).

Fields of papers citing papers by Canyi Lu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Canyi Lu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Canyi Lu. The network helps show where Canyi Lu may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Canyi Lu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Canyi Lu Line = papers co-authored together Canyi Lu links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 202314
2 202311
3 202310
4 202123
5 2019113
6 201834
7 20163
8 2016281
9 2016145
10
Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Tensors via Convex Optimization
Hit paper breakdown →
2016394
11 20167
12 201572
13 2015134
14 2014209
15 2014178
16 201411
17 201375
18 201316
19 201356
20 2012177

About Canyi Lu

Canyi Lu is a scholar working on Computational Mathematics, Computer Vision and Pattern Recognition, Computational Mechanics, Signal Processing and Media Technology, having authored 33 papers that have together received 3.8k indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (21 papers), Face and Expression Recognition (13 papers), Blind Source Separation Techniques (6 papers), Tensor decomposition and applications (5 papers), Advanced Image and Video Retrieval Techniques (5 papers), Image and Signal Denoising Methods (4 papers), Remote-Sensing Image Classification (4 papers) and Face recognition and analysis (3 papers). The work is most often cited by research in Computational Mathematics (986 citations), Computer Vision and Pattern Recognition (2.3k citations), Computational Mechanics (1.7k citations), Media Technology (659 citations) and Signal Processing (460 citations). Canyi Lu has collaborated with scholars based in China, Singapore and United States. Frequent co-authors include Shuicheng Yan, Zhouchen Lin, Jiashi Feng, Yudong Chen, Wei Liu, Yunchao Wei, Pan Zhou, Tao Mei, Jinhui Tang and Chao Zhang. Their work appears in journals such as IEEE Transactions on Circuits and Systems for Video Technology, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Cybernetics, IEEE Transactions on Image Processing and Machine Learning.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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