Leejay Wu
Impact in
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- Time Series Analysis and Forecasting
- Data Management and Algorithms
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- Image Retrieval and Classification Techniques
- Face and Expression Recognition
- Advanced Image and Video Retrieval Techniques
Papers in
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- Music and Audio Processing 2
- Data Management and Algorithms 1
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- Neural Networks and Applications 4
- Algorithms and Data Compression 3
- Co-authors
- Christos Faloutsos (7 shared papers)Caetano Traina (3 shared papers)Agma J. M. Traina (3 shared papers)Angeline M.L. Wong (1 shared paper)Phillip B. Gibbons (1 shared paper)Terry R. Payne (1 shared paper)Alex Waibel (1 shared paper)Katia Sycara (1 shared paper)
- Journals
- Data Mining and Knowledge Discovery (1 paper)Information Processing Letters (1 paper)ePrints Soton (University of Southampton) (1 paper)Figshare (1 paper)Cadernos de Linguística e Teoria da Literatura (Universidade Federal de Minas Gerais) (1 paper)
- Partner nations
- United StatesBrazil
In The Last Decade
Leejay Wu
8 papers receiving 83 citations
Peers
Comparison fields: 5 of 48
- Signal Processing 25
- Computer Vision and Pattern Recognition 34
- Artificial Intelligence 45
- Space and Planetary Science 1
- Statistical and Nonlinear Physics 9
Countries citing papers authored by Leejay Wu
This map shows the geographic impact of Leejay Wu'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 Leejay Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Leejay Wu more than expected).
Fields of papers citing papers by Leejay Wu
This network shows the impact of papers produced by Leejay Wu. 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 Leejay Wu. The network helps show where Leejay Wu may publish in the future.
Co-authors
The 9 scholars most cited alongside Leejay Wu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2007 | 30 | |
| 2 | 2018 | 22 | |
| 3 | 2005 | 22 | |
| 4 | 2018 | 9 | |
| 5 | 2002 | 4 | |
| 6 | Multimedia Queries by Example and Relevance Feedback | 2001 | 2 |
| 7 | 2002 | 2 | |
| 8 | Automated modeling and nonlinear axis scaling | 2005 | 1 |
About Leejay Wu
Leejay Wu is a scholar working on Signal Processing, Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems and Condensed Matter Physics, having authored 8 papers that have together received 92 indexed citations. Recurring topics across this work include Neural Networks and Applications (4 papers), Algorithms and Data Compression (3 papers), Image Retrieval and Classification Techniques (2 papers), Data Mining Algorithms and Applications (2 papers), Music and Audio Processing (2 papers), Image and Video Quality Assessment (1 paper), Data Management and Algorithms (1 paper) and Face and Expression Recognition (1 paper). The work is most often cited by research in Signal Processing (25 citations), Computer Vision and Pattern Recognition (34 citations), Artificial Intelligence (45 citations), Space and Planetary Science (1 citation) and Statistical and Nonlinear Physics (9 citations). Leejay Wu has collaborated with scholars based in United States and Brazil. Frequent co-authors include Christos Faloutsos, Caetano Traina, Agma J. M. Traina, Angeline M.L. Wong, Phillip B. Gibbons, Terry R. Payne, Alex Waibel, Katia Sycara and Jie Yang. Their work appears in journals such as Data Mining and Knowledge Discovery, Information Processing Letters, ePrints Soton (University of Southampton), Figshare and Cadernos de Linguística e Teoria da Literatura (Universidade Federal de Minas Gerais).
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.