Purushottam Kar
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 5%
- Information Systems top 5%
- Computational Mechanics top 10%
- Signal Processing top 10%
- Co-authors
- Prateek JainInderjit S. DhillonHsiang‐Fu YuKush BhatiaManik VarmaHimanshu JainHarish KarnickHarikrishna Narasimhan
- Topics
- Machine Learning and Algorithms (12 papers)Sparse and Compressive Sensing Techniques (6 papers)Machine Learning and Data Classification (6 papers)
- Partner nations
- IndiaUnited StatesUnited Kingdom
In The Last Decade
Purushottam Kar
29 papers receiving 982 citations
Peers
Comparison fields: 5 of 94
- Artificial Intelligence 647
- Computer Vision and Pattern Recognition 311
- Information Systems 149
- Computational Mechanics 145
- Signal Processing 90
Countries citing papers authored by Purushottam Kar
This map shows the geographic impact of Purushottam Kar'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 Purushottam Kar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Purushottam Kar more than expected).
Fields of papers citing papers by Purushottam Kar
This network shows the impact of papers produced by Purushottam Kar. 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 Purushottam Kar. The network helps show where Purushottam Kar may publish in the future.
Co-authorship network of co-authors of Purushottam Kar
This figure shows the co-authorship network connecting the top 25 collaborators of Purushottam Kar. A scholar is included among the top collaborators of Purushottam Kar 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 Purushottam Kar. Purushottam Kar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 2 | |
| 4 | 1 | |
| 5 | 3 | |
| 6 | 8 | |
| 7 | 28 | |
| 8 | SiameseXML: Siamese Networks meet Extreme Classifiers with 100M Labels | 9 |
| 9 | 15 | |
| 10 | Consistent Robust Regression | 15 |
| 11 | Stochastic Optimization Techniques for Quantification Performance Measures | 4 |
| 12 | 19 | |
| 13 | Sparse local embeddings for extreme multi-label classification | 176 |
| 14 | 28 | |
| 15 | 17 | |
| 16 | Random Feature Maps for Dot Product Kernels | 48 |
| 17 | 5 | |
| 18 | 13 | |
| 19 | Random Projection Trees Revisited | 4 |
| 20 | 1 |
About Purushottam Kar
Purushottam Kar is a scholar working on Software, Artificial Intelligence and Management Science and Operations Research, having authored 32 papers that have together received 1.0k indexed citations. Recurring topics across this work include Machine Learning and Algorithms (12 papers), Sparse and Compressive Sensing Techniques (6 papers) and Machine Learning and Data Classification (6 papers). The work is most often cited by research in Artificial Intelligence (647 citations), Computational Mathematics (11 citations) and Computer Vision and Pattern Recognition (311 citations). Purushottam Kar has collaborated with scholars based in India, United States and United Kingdom. Frequent co-authors include Prateek Jain, Inderjit S. Dhillon, Hsiang‐Fu Yu, Kush Bhatia, Prateek Jain, Manik Varma, Himanshu Jain, Harish Karnick, Prateek Jain and Harikrishna Narasimhan. Their work appears in journals such as Communications of the ACM, Machine Learning and Atmospheric measurement techniques.
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.