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.
Bayesian Compressive Sensing Using Laplace Priors
2009579 citationsS. Derin Babacan, Rafael Molina et al.IEEE Transactions on Image Processingprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by S. Derin Babacan
Since
Specialization
Citations
This map shows the geographic impact of S. Derin Babacan'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 S. Derin Babacan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites S. Derin Babacan more than expected).
Fields of papers citing papers by S. Derin Babacan
This network shows the impact of papers produced by S. Derin Babacan. 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 S. Derin Babacan. The network helps show where S. Derin Babacan may publish in the future.
Co-authorship network of co-authors of S. Derin Babacan
This figure shows the co-authorship network connecting the top 25 collaborators of S. Derin Babacan.
A scholar is included among the top collaborators of S. Derin Babacan 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 S. Derin Babacan. S. Derin Babacan is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Nakajima, Shinichi, Akiko Takeda, S. Derin Babacan, Masashi Sugiyama, & Ichiro Takeuchi. (2013). Global Solver and Its Efficient Approximation for Variational Bayesian Low-rank Subspace Clustering. Neural Information Processing Systems. 26. 1439–1447.4 indexed citations
4.
Nakajima, Shinichi, Masashi Sugiyama, & S. Derin Babacan. (2012). Sparse Additive Matrix Factorization for Robust PCA and Its Generalization. Asian Conference on Machine Learning. 301–316.2 indexed citations
5.
Babacan, S. Derin, Shinichi Nakajima, & N. Minh. (2012). Probabilistic Low-Rank Subspace Clustering. Neural Information Processing Systems. 25. 2744–2752.14 indexed citations
6.
Nakajima, Shinichi, Ryota Tomioka, Masashi Sugiyama, & S. Derin Babacan. (2012). Perfect Dimensionality Recovery by Variational Bayesian PCA. Neural Information Processing Systems. 25. 971–979.13 indexed citations
Nakajima, Shinichi, Masashi Sugiyama, & S. Derin Babacan. (2011). Global Solution of Fully-Observed Variational Bayesian Matrix Factorization is Column-Wise Independent. Tokyo Tech Research Repository (Tokyo Institute of Technology). 24. 208–216.4 indexed citations
11.
Mateos, Javier, et al.. (2011). Space-variant kernel deconvolution for dual exposure problem. European Signal Processing Conference. 1678–1682.2 indexed citations
Babacan, S. Derin, Rafael Molina, & Aggelos K. Katsaggelos. (2010). Variational Bayesian Super Resolution. IEEE Transactions on Image Processing. 20(4). 984–999.173 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.