Deep Canonical Correlation Analysis
Impact in
Classified as
- Journal
- International Conference on Machine Learning
In The Last Decade
doi.org/w4212882 →Countries where authors are citing Deep Canonical Correlation Analysis
This map shows the geographic impact of Deep Canonical Correlation Analysis. 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 Deep Canonical Correlation Analysis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Deep Canonical Correlation Analysis more than expected).
Fields of papers citing Deep Canonical Correlation Analysis
This network shows the impact of Deep Canonical Correlation Analysis. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Deep Canonical Correlation Analysis.
About Deep Canonical Correlation Analysis
This paper, published in 2013, received 815 indexed citations . Written by Galen Andrew, Raman Arora, Jeff Bilmes and Karen Livescu covering the research area of Signal Processing, Artificial Intelligence and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (542 citations), Artificial Intelligence (377 citations), Signal Processing (125 citations), Cognitive Neuroscience (57 citations) and Media Technology (46 citations). Published in International Conference on Machine Learning.
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/w4212882.