Kejun Huang

3.4k total citations · 1 hit paper
53 papers, 2.1k citations indexed

About

Kejun Huang is a scholar working on Computational Mechanics, Signal Processing and Computational Mathematics. According to data from OpenAlex, Kejun Huang has authored 53 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Computational Mechanics, 19 papers in Signal Processing and 18 papers in Computational Mathematics. Recurrent topics in Kejun Huang's work include Sparse and Compressive Sensing Techniques (23 papers), Blind Source Separation Techniques (18 papers) and Tensor decomposition and applications (18 papers). Kejun Huang is often cited by papers focused on Sparse and Compressive Sensing Techniques (23 papers), Blind Source Separation Techniques (18 papers) and Tensor decomposition and applications (18 papers). Kejun Huang collaborates with scholars based in United States, Hong Kong and Greece. Kejun Huang's co-authors include Nicholas D. Sidiropoulos, Xiao Fu, Christos Faloutsos, Evangelos E. Papalexakis, Lieven De Lathauwer, Ananthram Swami, Wing‐Kin Ma, Athanasios P. Liavas, Yonina C. Eldar and Mingyi Hong and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Signal Processing and IEEE Signal Processing Magazine.

In The Last Decade

Kejun Huang

48 papers receiving 2.1k citations

Hit Papers

align trajectories

What are hit papers?

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.

Tensor Decomposition for Signal Processing and Machine Learning

2017 981 citations Nicholas D. Sidiropoulos, Lieven De Lathauwer et al. IEEE Transactions on Signal Processing profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Kejun Huang United States	18	922	680	545	459	396	53	2.1k
Shuchin Aeron United States	16	1.0k 1.1×	1.1k 1.6×	255 0.5×	740 1.6×	283 0.7×	87	2.0k
Yangyang Xu United States	18	476 0.5×	1.4k 2.0×	309 0.6×	793 1.7×	506 1.3×	72	2.5k
Xiao Fu United States	25	1.0k 1.1×	882 1.3×	713 1.3×	681 1.5×	701 1.8×	130	3.7k
Yaniv Plan Canada	12	126 0.1×	1.5k 2.1×	508 0.9×	592 1.3×	345 0.9×	25	2.2k
Bernard Mourrain France	28	508 0.6×	1.5k 2.2×	206 0.4×	343 0.7×	304 0.8×	157	3.0k
Nicolas Gillis Belgium	23	171 0.2×	588 0.9×	384 0.7×	668 1.5×	316 0.8×	100	1.9k
Yuejie Chi United States	26	90 0.1×	1.6k 2.3×	968 1.8×	557 1.2×	445 1.1×	127	3.0k
Hédy Attouch France	22	138 0.1×	1.5k 2.2×	119 0.2×	465 1.0×	273 0.7×	44	3.3k
Raghunandan H. Keshavan United States	7	104 0.1×	821 1.2×	323 0.6×	393 0.9×	249 0.6×	11	1.3k
Shoham Sabach Israel	18	89 0.1×	870 1.3×	146 0.3×	414 0.9×	232 0.6×	37	2.1k

Countries citing papers authored by Kejun Huang

Since Specialization

Citations

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

Fields of papers citing papers by Kejun Huang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kejun Huang. 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 Kejun Huang. The network helps show where Kejun Huang may publish in the future.

Co-authorship network of co-authors of Kejun Huang

This figure shows the co-authorship network connecting the top 25 collaborators of Kejun Huang. A scholar is included among the top collaborators of Kejun Huang 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 Kejun Huang. Kejun Huang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Deng, Jin, et al.. (2025). Temporal constrained joint nonnegative matrix factorization for imaging genomic study of early mild cognitive impairment. Neurocomputing. 653. 131121–131121.

Huang, Kejun, et al.. (2024). Frank-Wolfe Algorithm for Simplicial and Nonnegative Component Analysis. 29. 1–5.

Huang, Kejun, et al.. (2024). Complex Bounded Component Analysis: Identifiability and Algorithm.

Huang, Kejun, et al.. (2023). Identifiable Bounded Component Analysis Via Minimum Volume Enclosing Parallelotope. PubMed. 2023. 3 indexed citations

Sun, Yuchen & Kejun Huang. (2023). Volume-Regularized Nonnegative Tucker Decomposition with Identifiability Guarantees. PubMed. 16. 1–5. 3 indexed citations

Sun, Yuchen & Kejun Huang. (2022). HOQRI: Higher-Order QR Iteration for Scalable Tucker Decomposition. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 3648–3652. 1 indexed citations

Fu, Xiao, Nico Vervliet, Lieven De Lathauwer, Kejun Huang, & Nicolas Gillis. (2020). Nonconvex Optimization Tools for Large-Scale Matrix and Tensor Decomposition with Structured Factors.. Lirias (KU Leuven). 1 indexed citations

Lu, Songtao, et al.. (2020). Finding Second-Order Stationary Points Efficiently in Smooth Nonconvex Linearly Constrained Optimization Problems. neural information processing systems. 33. 2811–2822. 1 indexed citations

Huang, Kejun & Xiao Fu. (2019). Detecting Overlapping and Correlated Communities without Pure Nodes: Identifiability and Algorithm. International Conference on Machine Learning. 2859–2868. 13 indexed citations

10.

Fu, Xiao, et al.. (2019). Block-randomized Stochastic Proximal Gradient for Constrained Low-rank Tensor Factorization. 36. 7485–7489. 5 indexed citations

11.

Huang, Kejun, Xiao Fu, & Nicholas D. Sidiropoulos. (2018). Learning Hidden Markov Models from Pairwise Co-occurrences with Application to Topic Modeling. International Conference on Machine Learning. 2068–2077. 3 indexed citations

12.

Fu, Xiao, Kejun Huang, & Nicholas D. Sidiropoulos. (2018). On Identifiability of Nonnegative Matrix Factorization. IEEE Signal Processing Letters. 25(3). 328–332. 57 indexed citations

13.

Huang, Kejun, Xiao Fu, & Nicholas D. Sidiropoulos. (2018). On Convergence of Epanechnikov Mean Shift. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1). 5 indexed citations

14.

Liavas, Athanasios P., et al.. (2017). Nesterov-Based Alternating Optimization for Nonnegative Tensor Factorization: Algorithm and Parallel Implementation. IEEE Transactions on Signal Processing. 66(4). 944–953. 27 indexed citations

15.

Fu, Xiao, Wing‐Kin Ma, Kejun Huang, & Nicholas D. Sidiropoulos. (2016). Robust volume minimization-based matrix factorization via alternating optimization. 53. 2534–2538. 7 indexed citations

16.

Huang, Kejun & Nicholas D. Sidiropoulos. (2016). Consensus-ADMM for General Quadratically Constrained Quadratic Programming. IEEE Transactions on Signal Processing. 64(20). 5297–5310. 163 indexed citations

17.

Sidiropoulos, Nicholas D., et al.. (2016). Least squares phase retrieval using feasible point pursuit. 35. 4288–4292. 3 indexed citations

18.

Huang, Kejun, Nicholas D. Sidiropoulos, Evangelos E. Papalexakis, et al.. (2015). Principled Neuro-Functional Connectivity Discovery. 631–639. 6 indexed citations

19.

Huang, Kejun, Nicholas D. Sidiropoulos, & Athanasios P. Liavas. (2015). Efficient algorithms for ‘universally’ constrained matrix and tensor factorization. 2521–2525. 4 indexed citations

20.

Fu, Xiao, Wing‐Kin Ma, Kejun Huang, & Nicholas D. Sidiropoulos. (2015). Blind Separation of Quasi-Stationary Sources: Exploiting Convex Geometry in Covariance Domain. IEEE Transactions on Signal Processing. 63(9). 2306–2320. 87 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.

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