Lishan Qiao

2.6k citations
72 papers · 1.9k indexed · 1 hit paper · h-index 18

Lishan Qiao

69 papers receiving 1.8k citations

Hit Papers

Sparsity preserving projections with applications to face...6352009202620142020200400600

Peers

Lishan Qiao
Comparison fields: 5 of 104
  • Computer Vision and Pattern Recognition 872
  • Media Technology 324
  • Cognitive Neuroscience 660
  • Computational Mathematics 12
  • Computational Mechanics 326
Replace Haixian Wang with:
Haixian Wang China
Kim‐Han Thung United States
Le An United States
Jiande Sun China
Shuaiqi Liu China
Samarendra Dandapat India
Zhuolin Jiang United States
Hwann-Tzong Chen Taiwan
Emad Fatemizadeh Iran
Zhiwu Huang China
Lishan Qiao relative to Haixian Wang China Haixian Wang's profile →
Citations per field
00.5×
Haixian Wang · 1×
Citations per year

Countries citing papers authored by Lishan Qiao

Since Specialization
Citations

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

Fields of papers citing papers by Lishan Qiao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Lishan Qiao. 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 Lishan Qiao. The network helps show where Lishan Qiao may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Lishan Qiao, 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 Lishan Qiao Line = papers co-authored together Lishan Qiao links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 20251
3 20241
4 20241
5 20241
6 20242
7 20246
8 202312
9 202314
10 202210
11 20215
12 202116
13 20214
14 202011
15 202027
16 202013
17 201843
18 201818
19 201621
20
Self-Dependent Locality Preserving Projection with Transformed Space-Oriented Neighborhood Graph
20100

About Lishan Qiao

Lishan Qiao is a scholar working on Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition, having authored 72 papers that have together received 1.9k indexed citations. Recurring topics across this work include Functional Brain Connectivity Studies (42 papers), EEG and Brain-Computer Interfaces (22 papers), Neural dynamics and brain function (19 papers), Face and Expression Recognition (12 papers), Advanced MRI Techniques and Applications (10 papers), Sparse and Compressive Sensing Techniques (7 papers), Advanced Neuroimaging Techniques and Applications (6 papers) and Mental Health Research Topics (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (872 citations), Media Technology (324 citations) and Cognitive Neuroscience (660 citations). Lishan Qiao has collaborated with scholars based in China, United States and South Korea. Frequent co-authors include Songcan Chen, Xiaoyang Tan, Dinggang Shen, Limei Zhang, Limei Zhang, Weikai Li, Mingxia Liu, Zhengxia Wang, Han Zhang and Minjeong Kim. Their work appears in journals such as PLoS ONE, NeuroImage and Brain Research.

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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