Spectral Regularization Algorithms for Learning Large Incomplete Matrices.

592 indexed citations
published 2010

Countries where authors are citing Spectral Regularization Algorithms for Learning Large Incomplete Matrices.

Specialization
Citations

This map shows the geographic impact of Spectral Regularization Algorithms for Learning Large Incomplete Matrices.. 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 Spectral Regularization Algorithms for Learning Large Incomplete Matrices. with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Spectral Regularization Algorithms for Learning Large Incomplete Matrices. more than expected).

Fields of papers citing Spectral Regularization Algorithms for Learning Large Incomplete Matrices.

Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Spectral Regularization Algorithms for Learning Large Incomplete Matrices.. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Spectral Regularization Algorithms for Learning Large Incomplete Matrices..

About Spectral Regularization Algorithms for Learning Large Incomplete Matrices.

This paper, published in 2010, received 592 indexed citations . Written by Rahul Mazumder, Trevor Hastie and Robert Tibshirani covering the research area of Numerical Analysis, Artificial Intelligence and Computational Mechanics. It is primarily cited by scholars working on Computational Mechanics (219 citations), Artificial Intelligence (183 citations) and Computer Vision and Pattern Recognition (178 citations). Published in PubMed.

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/w84215019.

Explore hit-papers with similar magnitude of impact

Rankless by CCL
2026