Robert J. Plemmons

179 papers receiving 6.2k citations

Robert J. Plemmons's Hit Papers

Algorithms and applications for approximate nonnegative matrix factorization 2006 · 1.1k citations
1.1k0+7+14Years since publication2505007501000

Peers

Robert J. Plemmons
Comparison fields: 5 of 175
  • Computational Mathematics 313
  • Numerical Analysis 942
  • Computational Theory and Mathematics 2.1k
  • Media Technology 998
  • Signal Processing 938
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Countries citing papers authored by Robert J. Plemmons

Since Specialization
Citations

This map shows the geographic impact of Robert J. Plemmons'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 Robert J. Plemmons with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Robert J. Plemmons more than expected).

Fields of papers citing papers by Robert J. Plemmons

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Robert J. Plemmons. 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 Robert J. Plemmons. The network helps show where Robert J. Plemmons may publish in the future.

Co-authors

The 25 scholars most cited alongside Robert J. Plemmons, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Robert J. Plemmons Line = papers co-authored together Robert J. Plemmons links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 189 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Algorithms and applications for approximate nonnegative matrix factorization
Hit paper breakdown →
20061143
2
Document clustering using nonnegative matrix factorization
Hit paper breakdown →
2005462
3 2005445
4 1977311
5 2004229
6 1979165
7 1987153
8 2013143
9 1990140
10 2009139
11 1978129
12 2003117
13
Optimality, computation, and interpretation of nonnegative matrix factorizations
2004102
14 199386
15 198985
16 198080
17 197077
18 197776
19 197873
20 199369

About Robert J. Plemmons

Robert J. Plemmons is a scholar working on Computational Theory and Mathematics, Media Technology, Computational Mechanics, Computer Vision and Pattern Recognition and Numerical Analysis, having authored 189 papers that have together received 7.1k indexed citations. Recurring topics across this work include Matrix Theory and Algorithms (74 papers), Sparse and Compressive Sensing Techniques (27 papers), Advanced Optimization Algorithms Research (27 papers), Remote-Sensing Image Classification (25 papers), Image and Signal Denoising Methods (19 papers), Statistical and numerical algorithms (18 papers), Advanced Image Fusion Techniques (14 papers) and Advanced Topics in Algebra (10 papers). The work is most often cited by research in Computational Mathematics (313 citations), Numerical Analysis (942 citations), Computational Theory and Mathematics (2.1k citations), Media Technology (998 citations) and Signal Processing (938 citations). Robert J. Plemmons has collaborated with scholars based in United States, Hong Kong and United Kingdom. Frequent co-authors include V. Paúl Pauca, Michael W. Berry, Abraham Berman, Amy N. Langville, Murray Browne, Jonathan Piper, Michael Neumann, James G. Nagy, R. E. Funderlic and Michael K. Ng. Their work appears in journals such as Linear Algebra and its Applications, Proceedings of the American Mathematical Society, Numerische Mathematik, Mathematics of Computation and SIAM Journal on Matrix Analysis and Applications.

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.

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