Jouko Lampinen

2.8k total citations
46 papers, 1.7k citations indexed

About

Jouko Lampinen is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Cognitive Neuroscience. According to data from OpenAlex, Jouko Lampinen has authored 46 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Artificial Intelligence, 11 papers in Computer Vision and Pattern Recognition and 11 papers in Cognitive Neuroscience. Recurrent topics in Jouko Lampinen's work include Neural Networks and Applications (11 papers), Fault Detection and Control Systems (8 papers) and Gaussian Processes and Bayesian Inference (8 papers). Jouko Lampinen is often cited by papers focused on Neural Networks and Applications (11 papers), Fault Detection and Control Systems (8 papers) and Gaussian Processes and Bayesian Inference (8 papers). Jouko Lampinen collaborates with scholars based in Finland, United States and Switzerland. Jouko Lampinen's co-authors include Aki Vehtari, Simo Särkkä, Mikko Sams, Erkki Oja, Iiro P. Jääskeläinen, Athanasios Gotsopoulos, Heini Saarimäki, Lauri Nummenmaa, Patrik Vuilleumier and Riitta Hari and has published in prestigious journals such as PLoS ONE, NeuroImage and IEEE Transactions on Geoscience and Remote Sensing.

In The Last Decade

Jouko Lampinen

45 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jouko Lampinen Finland 17 559 466 250 195 156 46 1.7k
Michael T. Rosenstein United States 21 742 1.3× 608 1.3× 268 1.1× 227 1.2× 66 0.4× 45 4.8k
Matthew B. Kennel United States 18 699 1.3× 714 1.5× 135 0.5× 521 2.7× 130 0.8× 29 3.9k
Yong Peng China 25 589 1.1× 685 1.5× 582 2.3× 188 1.0× 91 0.6× 137 2.0k
Mauricio A. Álvarez Colombia 15 471 0.8× 155 0.3× 136 0.5× 105 0.5× 65 0.4× 80 1.3k
Jesse S. Jin Australia 25 241 0.4× 279 0.6× 1.2k 5.0× 274 1.4× 171 1.1× 126 2.3k
Michael W. Trosset United States 25 437 0.8× 397 0.9× 122 0.5× 42 0.2× 231 1.5× 73 3.0k
Jonathan A. Marshall United States 10 675 1.2× 354 0.8× 441 1.8× 175 0.9× 91 0.6× 24 2.0k
Ana Maria Tomé Portugal 21 271 0.5× 468 1.0× 324 1.3× 326 1.7× 24 0.2× 136 1.5k
Xavier Alameda-Pineda France 24 582 1.0× 138 0.3× 895 3.6× 422 2.2× 55 0.4× 69 2.0k
Frédéric Maire Australia 17 349 0.6× 120 0.3× 331 1.3× 50 0.3× 59 0.4× 83 1.2k

Countries citing papers authored by Jouko Lampinen

Since Specialization
Citations

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

Fields of papers citing papers by Jouko Lampinen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jouko Lampinen

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

All Works

20 of 20 papers shown
1.
Bogert, Brigitte, Benjamin P. Gold, Mikko Sams, et al.. (2016). Hidden sources of joy, fear, and sadness: Explicit versus implicit neural processing of musical emotions. Neuropsychologia. 89. 393–402. 48 indexed citations
2.
Lahnakoski, Juha M., Juha Salmi, Iiro P. Jääskeläinen, et al.. (2012). Stimulus-Related Independent Component and Voxel-Wise Analysis of Human Brain Activity during Free Viewing of a Feature Film. PLoS ONE. 7(4). e35215–e35215. 45 indexed citations
3.
Auranen, Toni, Aapo Nummenmaa, Simo Vanni, et al.. (2008). Automatic fMRI‐guided MEG multidipole localization for visual responses. Human Brain Mapping. 30(4). 1087–1099. 15 indexed citations
4.
Auranen, Toni, Aapo Nummenmaa, Matti Hämäläinen, et al.. (2007). Bayesian inverse analysis of neuromagnetic data using cortically constrained multiple dipoles. Human Brain Mapping. 28(10). 979–994. 13 indexed citations
5.
Nummenmaa, Aapo, Toni Auranen, Matti Hämäläinen, et al.. (2007). Automatic relevance determination based hierarchical Bayesian MEG inversion in practice. NeuroImage. 37(3). 876–889. 23 indexed citations
6.
Nummenmaa, Aapo, Toni Auranen, Matti Hämäläinen, et al.. (2007). Hierarchical Bayesian estimates of distributed MEG sources: Theoretical aspects and comparison of variational and MCMC methods. NeuroImage. 35(2). 669–685. 50 indexed citations
7.
Auranen, Toni, Aapo Nummenmaa, Matti Hämäläinen, et al.. (2005). Bayesian analysis of the neuromagnetic inverse problem with ℓ-norm priors. NeuroImage. 26(3). 870–884. 48 indexed citations
8.
Särkkä, Simo, Aki Vehtari, & Jouko Lampinen. (2005). Rao-Blackwellized particle filter for multiple target tracking. Information Fusion. 8(1). 2–15. 214 indexed citations
9.
Särkkä, Simo, Aki Vehtari, & Jouko Lampinen. (2004). Rao-Blackwellized Monte Carlo Data Association for Multiple Target Tracking. 61 indexed citations
10.
Lampinen, Jouko, et al.. (2003). Reversible jump MCMC for two-state multivariate poisson mixtures. Kybernetika. 39(3). 307–315.
11.
Lampinen, Jouko, et al.. (2002). On the generative probability density model in the self-organizing map. Neurocomputing. 48(1-4). 217–228. 16 indexed citations
12.
Lampinen, Jouko & Aki Vehtari. (2001). Bayesian approach for neural networks—review and case studies. Neural Networks. 14(3). 257–274. 265 indexed citations
13.
Lampinen, Jouko, et al.. (2001). <title>Bayesian object matching based on MCMC sampling and Gabor filters</title>. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 4572. 41–50. 3 indexed citations
14.
Lampinen, Jouko, et al.. (2001). <title>3D object recognition based on hierarchical eigen shapes and Bayesian inference</title>. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 4572. 165–173. 2 indexed citations
15.
Lampinen, Jouko & Aki Vehtari. (2000). Bayesian techniques for neural networks — Review and case studies. European Signal Processing Conference. 1–8. 5 indexed citations
16.
Lampinen, Jouko, et al.. (2000). Self-Organizing Maps in data analysis - notes on overfitting and overinterpretation.. The European Symposium on Artificial Neural Networks. 239–244. 7 indexed citations
17.
Varsta, Markus, Jukka Heikkonen, & Jouko Lampinen. (2000). Analytical comparison of the Temporal Kohonen Map and the Recurrent Self Organizing Map.. The European Symposium on Artificial Neural Networks. 273–280. 5 indexed citations
18.
Vehtari, Aki & Jouko Lampinen. (2000). Bayesian MLP neural networks for image analysis. Pattern Recognition Letters. 21(13-14). 1183–1191. 31 indexed citations
19.
Lampinen, Jouko, et al.. (1994). <title>Wood defect recognition with self-organizing feature selection</title>. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 2353. 385–395. 3 indexed citations
20.
Lampinen, Jouko. (1992). Neural pattern recognition : distortion tolerance by self-organizing maps. 4 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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