Kuang-Chih Lee

5.4k total citations · 1 hit paper
30 papers, 3.4k citations indexed

About

Kuang-Chih Lee is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Information Systems. According to data from OpenAlex, Kuang-Chih Lee has authored 30 papers receiving a total of 3.4k indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Computer Vision and Pattern Recognition, 8 papers in Artificial Intelligence and 6 papers in Information Systems. Recurrent topics in Kuang-Chih Lee's work include Face and Expression Recognition (7 papers), Face recognition and analysis (6 papers) and Recommender Systems and Techniques (5 papers). Kuang-Chih Lee is often cited by papers focused on Face and Expression Recognition (7 papers), Face recognition and analysis (6 papers) and Recommender Systems and Techniques (5 papers). Kuang-Chih Lee collaborates with scholars based in United States, China and Canada. Kuang-Chih Lee's co-authors include David Kriegman, Ming–Hsuan Yang, Jongwoo Lim, Jeffrey Ho, Ali Dasdan, Wentong Li, Donghyuk Shin, Suleyman Cetintas, Andrew B. Whinston and Gene Moo Lee and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, MIS Quarterly and Computer Vision and Image Understanding.

In The Last Decade

Kuang-Chih Lee

29 papers receiving 3.2k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Acquiring linear subspaces for face recognition under variable lighting

2005 1.7k citations Kuang-Chih Lee, David Kriegman et al. IEEE Transactions on Pattern Analysis and Machine Intelligence profile →

Peers

Kuang-Chih Lee

CVPR

CM

AI

SP

MT

Kai Yu Germany

Benyu Zhang China

Rong Jin United States

Zhiyong Cheng China

Xiaofeng He China

Prateek Jain United States

Sugato Basu United States

J.J. Hull United States

Quanquan Gu United States

Kai Yu Germany

Kuang-Chih Lee

1.1k ×0.4

CVPR

273 ×0.3

CM

1.7k ×2.5

AI

207 ×0.4

SP

151 ×0.3

MT

Citations per year, relative to Kuang-Chih Lee Kuang-Chih Lee (= 1×) peers Kai Yu

Countries citing papers authored by Kuang-Chih Lee

Since Specialization

Citations

This map shows the geographic impact of Kuang-Chih Lee'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 Kuang-Chih Lee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kuang-Chih Lee more than expected).

Fields of papers citing papers by Kuang-Chih Lee

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kuang-Chih Lee. 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 Kuang-Chih Lee. The network helps show where Kuang-Chih Lee may publish in the future.

Co-authorship network of co-authors of Kuang-Chih Lee

This figure shows the co-authorship network connecting the top 25 collaborators of Kuang-Chih Lee. A scholar is included among the top collaborators of Kuang-Chih Lee based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Kuang-Chih Lee. Kuang-Chih Lee is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Corizzo, Roberto, Junfeng Ge, Colin Bellinger, et al.. (2022). 4th Workshop on Deep Learning Practice and Theory for High-Dimensional Sparse and Imbalanced Data with KDD 2022. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4860–4861. 1 indexed citations

2.

Wang, Pengjie, Peiyan Zhang, Wei Lin, et al.. (2021). Learning Effective and Efficient Embedding via an Adaptively-Masked Twins-based Layer. 3568–3572. 7 indexed citations

3.

Wang, Pengjie, Jinquan Liu, Wei Lin, et al.. (2021). Binary Code based Hash Embedding for Web-scale Applications. 3563–3567. 9 indexed citations

4.

Wen, Zheng, et al.. (2021). Optimal Bidding, Allocation, and Budget Spending for a Demand-Side Platform with Generic Auctions. SSRN Electronic Journal.

5.

Shin, Donghyuk, Shu He, Gene Moo Lee, et al.. (2020). Enhancing Social Media Analysis with Visual Data Analytics: A Deep Learning Approach. MIS Quarterly. 44(4). 1459–1492. 140 indexed citations

6.

Sun, Changzhi, Yuanbin Wu, Man Lan, et al.. (2018). Extracting Entities and Relations with Joint Minimum Risk Training. 32 indexed citations

7.

Zhang, Qizhi, et al.. (2018). Large Scale Classification in Deep Neural Network with Label Mapping. 1134–1143. 9 indexed citations

8.

Shin, Donghyuk, Shu He, Gene Moo Lee, et al.. (2016). Content Complexity, Similarity, and Consistency in Social Media: A Deep Learning Approach. SSRN Electronic Journal. 7 indexed citations

9.

Wang, Liang, Kuang-Chih Lee, & Quan Lu. (2016). Improving Advertisement Recommendation by Enriching User Browser Cookie Attributes. 2401–2404. 3 indexed citations

10.

Xu, Jian, et al.. (2016). Lift-Based Bidding in Ad Selection. Proceedings of the AAAI Conference on Artificial Intelligence. 30(1). 11 indexed citations

11.

Shin, Donghyuk, Suleyman Cetintas, Kuang-Chih Lee, & Inderjit S. Dhillon. (2015). Tumblr Blog Recommendation with Boosted Inductive Matrix Completion. 203–212. 23 indexed citations

12.

Lee, Kuang-Chih, et al.. (2012). Estimating conversion rate in display advertising from past erformance data. 768–776. 124 indexed citations

13.

Lee, Kuang-Chih, et al.. (2008). A bottom-up framework for robust facial feature detection. 2. 1–6. 8 indexed citations

14.

Lee, Kuang-Chih, et al.. (2005). Acquiring linear subspaces for face recognition under variable lighting. IEEE Transactions on Pattern Analysis and Machine Intelligence. 27(5). 684–698. 1696 indexed citations breakdown →

15.

Lee, Kuang-Chih, Jeffrey Ho, & David Kriegman. (2005). Nine points of light: acquiring subspaces for face recognition under variable lighting. 1. I–519. 86 indexed citations

16.

Ho, Jeffrey, Kuang-Chih Lee, & David Kriegman. (2005). Compressing large polygonal models. 357–573. 10 indexed citations

17.

Lee, Kuang-Chih, Jeffrey Ho, Ming–Hsuan Yang, & David Kriegman. (2005). Visual tracking and recognition using probabilistic appearance manifolds. Computer Vision and Image Understanding. 99(3). 303–331. 144 indexed citations

18.

Lee, Kuang-Chih, et al.. (2004). Visual tracking using learned linear subspaces. 1. 782–789. 123 indexed citations

19.

Lim, Jongwoo, et al.. (2003). Clustering appearances of objects under varying illumination conditions. I–11. 373 indexed citations

20.

Ho, Jeffrey, Kuang-Chih Lee, & David Kriegman. (2001). On Reducing the Complexity of Illumination Cones for Face Recognition. 2 indexed citations

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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