Erkki Oja
About
In The Last Decade
Erkki Oja
196 papers receiving 21.2k citations
Hit Papers
Peers
Comparison fields: 5 of 208
- Signal Processing 10.2k
- Artificial Intelligence 6.7k
- Computer Vision and Pattern Recognition 5.6k
- Cognitive Neuroscience 5.1k
- Analytical Chemistry 3.5k
Countries citing papers authored by Erkki Oja
This map shows the geographic impact of Erkki Oja'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 Erkki Oja with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Erkki Oja more than expected).
Fields of papers citing papers by Erkki Oja
This network shows the impact of papers produced by Erkki Oja. 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 Erkki Oja. The network helps show where Erkki Oja may publish in the future.
Co-authorship network of co-authors of Erkki Oja
This figure shows the co-authorship network connecting the top 25 collaborators of Erkki Oja. A scholar is included among the top collaborators of Erkki Oja 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 Erkki Oja. Erkki Oja is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Clustering by Nonnegative Matrix Factorization Using Graph Random Walk | 48 |
| 2 | 47 | |
| 3 | Solving Large Regression Problems using an Ensemble of GPU-accelerated ELMs | 11 |
| 4 | 1 | |
| 5 | Artificial Neural Networks: Formal Models and Their Applications ICANN 2005: 15th International Conference, Warsaw, Poland, September 11-15, 2005, Proceedings, ... Part II (Lecture Notes in Computer Science) | 2 |
| 6 | 1 | |
| 7 | 5 | |
| 8 | Artefact Detection In Astrophysical Image Data Using Independent Component Analysis | 1 |
| 9 | Dynamical Factor Analysis Of Rhythmic Magnetoencephalographic Activity | 8 |
| 10 | 16 | |
| 11 | Blind separation from ε-contaminated mixtures | 2 |
| 12 | Independent Component Analysis for Parallel Financial Time Series | 69 |
| 13 | Independent Component Analysis for Identification of Artifacts in Magnetoencephalographic Recordings | 156 |
| 14 | S-Map: A Network with a Simple Self-Organization Algorithm for Generative Topographic Mappings | 9 |
| 15 | One-unit Learning Rules for Independent Component Analysis | 32 |
| 16 | Principal component analysis by homogeneous neural networks, part II: Analysis and extentions of the learning algorithm | 39 |
| 17 | Principal component analysis by homogeneous neural networks, Part I : The weighted subspace criterion | 44 |
| 18 | Randomized Hough Transform (RHT) in Engineering Drawing Vectorization System | 8 |
| 19 | Basic principles of image analysis by a computer | 1 |
| 20 | Mathematical background to stereology and morphometry for diagnostic pathologists | 11 |
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.