Robert J. Plemmons

Computer Vision and Pattern Recognition · 2004–2007

A researcher in the Science corpus: 5 papers; trusts 14 researchers and is trusted by 34 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 12 citations into it; #5,067 of 21,575 by trust here, #5,565 by citations
• Representation & Self-Supervised Learning — 12 citations into it
• Signal Processing — 10 citations into it
• Artificial Intelligence — 9 citations into it; #9,385 of 35,345 by trust here, #13,353 by citations
• Media Technology — 7 citations into it; #911 of 3,621 by trust here, #1,598 by citations
• Optimization & Training — 6 citations into it

Whom Robert J. Plemmons trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Patrik O. Hoyer
2. Daniel D. Lee
3. Aapo Hyvärinen
4. Farial Shahnaz
5. David L. Donoho
6. V. P. Pauca
7. Nirmal Keshava
8. Chih-Jen Lin
9. Hans-Jürgen Sebastian

Who trusts Robert J. Plemmons overall

1. Hyunsoo Kim
2. Haesun Park
3. Yangyang Xu
4. Jingu Kim
5. Sen Jia
6. Y. Qian
7. Naiyang Guan
8. Karthik Devarajan
9. Christos Boutsidis
10. Chris H. Q. Ding

Papers from Robert J. Plemmons's perspective

Ordered by how strongly the researchers Robert J. Plemmons trusts endorse each paper. Same corpus, a different order for every researcher.

1. Learning the parts of objects by non-negative matrix factorization
2. Algorithms for Non-negative Matrix Factorization
3. Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values†
4. Non-negative sparse coding
5. Non-negative Matrix Factorization with Sparseness Constraints (Journal of machine learning research)
6. Emergence of Phase- and Shift-Invariant Features by Decomposition of Natural Images into Independent Feature Subspaces
7. Document clustering based on non-negative matrix factorization
8. On the convergence of the block nonlinear Gauss-Seidel method under convex constraints (Operations Research Letters)
9. Text Mining Using Non-Negative Matrix Factorizations (SDM)
10. Projected Gradient Methods for Nonnegative Matrix Factorization

Their papers

• Algorithms and applications for approximate nonnegative matrix factorization (2007) — 1,648 citations
• Document clustering using nonnegative matrix factorization (2006) — 681 citations
• On reduced rank nonnegative matrix factorization for symmetric nonnegative matrices (2004) — 68 citations
• Text Mining Using Non-Negative Matrix Factorizations (2004) — 365 citations
• Nonnegative matrix factorization for spectral data analysis (2006) — 649 citations