Co-regularized Multi-view Spectral Clustering
Impact in
Classified as
- Journal
- Neural Information Processing Systems
In The Last Decade
doi.org/w3296962 →Countries where authors are citing Co-regularized Multi-view Spectral Clustering
This map shows the geographic impact of Co-regularized Multi-view Spectral Clustering. 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 Co-regularized Multi-view Spectral Clustering with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Co-regularized Multi-view Spectral Clustering more than expected).
Fields of papers citing Co-regularized Multi-view Spectral Clustering
This network shows the impact of Co-regularized Multi-view Spectral Clustering. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Co-regularized Multi-view Spectral Clustering.
About Co-regularized Multi-view Spectral Clustering
This paper, published in 2011, received 691 indexed citations . Written by Abhishek Kumar, Piyush Rai and Hal Daumé covering the research area of Artificial Intelligence and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (521 citations), Artificial Intelligence (363 citations), Media Technology (147 citations), Statistical and Nonlinear Physics (91 citations) and Urban Studies (82 citations). Published in Neural Information Processing Systems.
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.
This paper is also available at doi.org/w3296962.