Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Dictionaries for Sparse Representation Modeling
2010892 citationsRon Rubinstein, Alfred M. Bruckstein⋆ et al.Proceedings of the IEEEprofile →
Countries citing papers authored by Ron Rubinstein
Since
Specialization
Citations
This map shows the geographic impact of Ron Rubinstein'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 Ron Rubinstein with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ron Rubinstein more than expected).
This network shows the impact of papers produced by Ron Rubinstein. 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 Ron Rubinstein. The network helps show where Ron Rubinstein may publish in the future.
Co-authorship network of co-authors of Ron Rubinstein
This figure shows the co-authorship network connecting the top 25 collaborators of Ron Rubinstein.
A scholar is included among the top collaborators of Ron Rubinstein based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Ron Rubinstein. Ron Rubinstein is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Rubinstein, Ron, et al.. (2012). Adaptive image compression using sparse dictionaries. International Conference on Systems, Signals and Image Processing. 592–595.29 indexed citations
3.
Rubinstein, Ron, Tomer Peleg, & Michael Elad. (2012). Analysis K-SVD: A Dictionary-Learning Algorithm for the Analysis Sparse Model. IEEE Transactions on Signal Processing. 61(3). 661–677.327 indexed citations breakdown →
4.
Rubinstein, Ron, Alfred M. Bruckstein⋆, & Michael Elad. (2010). Dictionaries for Sparse Representation Modeling Digital sampling can display signals, and it should be possible to expose a large part of the desired signal information with only a limited signal sample..2 indexed citations
5.
Rubinstein, Ron, Alfred M. Bruckstein⋆, & Michael Elad. (2010). Dictionaries for Sparse Representation Modeling. Proceedings of the IEEE. 98(6). 1045–1057.892 indexed citations breakdown →
6.
Rubinstein, Ron, Michael Zibulevsky, & Michael Elad. (2009). Learning Sparse Dictionaries for Sparse Signal Approximation.10 indexed citations
7.
Rubinstein, Ron, Michael Zibulevsky, & Michael Elad. (2009). Double Sparsity: Learning Sparse Dictionaries for Sparse Signal Approximation. IEEE Transactions on Signal Processing. 58(3). 1553–1564.428 indexed citations breakdown →
8.
Rubinstein, Ron, Michael Zibulevsky, & Michael Elad. (2008). E-cient Implementation of the K-SVD Algorithm and the Batch-OMP Method.6 indexed citations
Elad, Michael, Peyman Milanfar, & Ron Rubinstein. (2007). Analysis versus synthesis in signal priors. Inverse Problems. 23(3). 947–968.434 indexed citations breakdown →
11.
Chisin, Roland, U. Pietrzyk, Jean‐Yves Sichel, et al.. (1993). Registration and display of multimodal images: applications in the extracranial head and neck region.. PubMed. 22(4). 214–9.10 indexed citations
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.