Richard A. Harshman
- Artificial Intelligence top 0.05%
- Information Systems top 0.1%
- Signal Processing top 0.2%
- Computer Vision and Pattern Recognition top 0.5%
- Computational Mathematics top 0.02%
- Co-authors
- George W. FurnasScott DeerwesterThomas K. LandauerSusan DumaisMargaret E. LundyAllan PaivioJackson T. GandourPeter Ladefoged
- Topics
- Tensor decomposition and applications (20 papers)Blind Source Separation Techniques (12 papers)Hemispheric Asymmetry in Neuroscience (6 papers)
- Journals
- Journal of the American Statistical AssociationNeuroImageThe Journal of the Acoustical Society of America
- Partner nations
- CanadaUnited StatesNetherlands
In The Last Decade
Richard A. Harshman
54 papers receiving 11.4k citations
Hit Papers
Peers
Comparison fields: 5 of 207
- Artificial Intelligence 6.3k
- Information Systems 2.6k
- Signal Processing 2.0k
- Computer Vision and Pattern Recognition 1.8k
- Computational Mathematics 1.7k
Countries citing papers authored by Richard A. Harshman
This map shows the geographic impact of Richard A. Harshman'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 Richard A. Harshman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Richard A. Harshman more than expected).
Fields of papers citing papers by Richard A. Harshman
This network shows the impact of papers produced by Richard A. Harshman. 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 Richard A. Harshman. The network helps show where Richard A. Harshman may publish in the future.
Co-authorship network of co-authors of Richard A. Harshman
This figure shows the co-authorship network connecting the top 25 collaborators of Richard A. Harshman. A scholar is included among the top collaborators of Richard A. Harshman 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 Richard A. Harshman. Richard A. Harshman is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 43 | |
| 2 | 9 | |
| 3 | 3 | |
| 4 | 81 | |
| 5 | 71 | |
| 6 | 18 | |
| 7 | Optimal solutions to non-negative PARAFAC/multilinear NMF always exist | 16 |
| 8 | 19 | |
| 9 | 23 | |
| 10 | 232 | |
| 11 | 18 | |
| 12 | 79 | |
| 13 | Indexing by latent semantic analysisbreakdown → | 7839 |
| 14 | How 3-MFA data can cause degenerate parafac solutions, among other relationships | 59 |
| 15 | 36 | |
| 16 | 2 | |
| 17 | 140 | |
| 18 | 114 | |
| 19 | 185 | |
| 20 | 131 |
About Richard A. Harshman
Richard A. Harshman is a scholar working on Computational Mathematics, Signal Processing and Experimental and Cognitive Psychology, having authored 56 papers that have together received 12.7k indexed citations. Recurring topics across this work include Tensor decomposition and applications (20 papers), Blind Source Separation Techniques (12 papers) and Hemispheric Asymmetry in Neuroscience (6 papers). The work is most often cited by research in Computational Mathematics (1.7k citations), Artificial Intelligence (6.3k citations) and Signal Processing (2.0k citations). Richard A. Harshman has collaborated with scholars based in Canada, United States and Netherlands. Frequent co-authors include George W. Furnas, Scott Deerwester, Thomas K. Landauer, Susan Dumais, Margaret E. Lundy, Allan Paivio, Jackson T. Gandour, Peter Ladefoged, Louis Goldstein and Christopher G. Thomas. Their work appears in journals such as Journal of the American Statistical Association, NeuroImage and The Journal of the Acoustical Society of America.
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.