Lexiang Ye
Impact in
- Signal Processing top 1%
- Time Series Analysis and Forecasting
- Music and Audio Processing
- Data Management and Algorithms
- Artificial Intelligence top 2%
- Anomaly Detection Techniques and Applications
- Advanced Text Analysis Techniques
Papers in
-
- Data Management and Algorithms 3
- Time Series Analysis and Forecasting 3
- Music and Audio Processing 1
- Co-authors
- Eamonn Keogh (7 shared papers)Xiaoyue Wang (5 shared papers)Agenor Mafra‐Neto (2 shared papers)Christian R. Shelton (2 shared papers)Dragomir Yankov (1 shared paper)
- Journals
- Data Mining and Knowledge Discovery (1 paper)RePEc: Research Papers in Economics (1 paper)
- Partner nations
- United States
In The Last Decade
Lexiang Ye
7 papers receiving 861 citations
Lexiang Ye's Hit Papers
Peers
Comparison fields: 5 of 86
- Signal Processing 708
- Artificial Intelligence 545
- Economics and Econometrics 198
- Computer Vision and Pattern Recognition 93
- Management Science and Operations Research 46
Countries citing papers authored by Lexiang Ye
This map shows the geographic impact of Lexiang Ye'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 Lexiang Ye with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lexiang Ye more than expected).
Fields of papers citing papers by Lexiang Ye
This network shows the impact of papers produced by Lexiang Ye. 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 Lexiang Ye. The network helps show where Lexiang Ye may publish in the future.
Co-authors
The 5 scholars most cited alongside Lexiang Ye, 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 | Time series shapelets Hit paper breakdown → | 2009 | 592 |
| 2 | 2010 | 275 | |
| 3 | 2008 | 10 | |
| 4 | Autocannibalistic and Anyspace Indexing Algorithms with Application to Sensor Data Mining. | 2009 | 7 |
| 5 | 2009 | 4 | |
| 6 | 2010 | 2 | |
| 7 | 2008 | 2 |
About Lexiang Ye
Lexiang Ye is a scholar working on Signal Processing, Artificial Intelligence, Computer Networks and Communications, Computer Vision and Pattern Recognition and Economics and Econometrics, having authored 7 papers that have together received 892 indexed citations. Recurring topics across this work include Data Management and Algorithms (3 papers), Time Series Analysis and Forecasting (3 papers), Video Analysis and Summarization (2 papers), Advanced Image and Video Retrieval Techniques (2 papers), Advanced Database Systems and Queries (2 papers), Image Retrieval and Classification Techniques (2 papers), Complex Systems and Time Series Analysis (2 papers) and Music and Audio Processing (1 paper). The work is most often cited by research in Signal Processing (708 citations), Artificial Intelligence (545 citations), Economics and Econometrics (198 citations), Computer Vision and Pattern Recognition (93 citations) and Management Science and Operations Research (46 citations). Lexiang Ye has collaborated with scholars based in United States. Frequent co-authors include Eamonn Keogh, Xiaoyue Wang, Agenor Mafra‐Neto, Christian R. Shelton and Dragomir Yankov. Their work appears in journals such as Data Mining and Knowledge Discovery and RePEc: Research Papers in Economics.
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.