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.
Canonical Correlation Analysis: An Overview with Application to Learning Methods
20042.2k citationsDavid R. Hardoon, Sándor Szedmák et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by David R. Hardoon
Since
Specialization
Citations
This map shows the geographic impact of David R. Hardoon'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 David R. Hardoon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David R. Hardoon more than expected).
Fields of papers citing papers by David R. Hardoon
This network shows the impact of papers produced by David R. Hardoon. 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 David R. Hardoon. The network helps show where David R. Hardoon may publish in the future.
Co-authorship network of co-authors of David R. Hardoon
This figure shows the co-authorship network connecting the top 25 collaborators of David R. Hardoon.
A scholar is included among the top collaborators of David R. Hardoon 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 David R. Hardoon. David R. Hardoon 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.
Ondruš, Jan, et al.. (2019). Overcoming Status Quo Bias: Nudging in a Government-Led Digital Transformation Initiative. Journal of the Association for Information Systems.4 indexed citations
Diethe, Tom, et al.. (2009). Matching Pursuit Kernel Fisher Discriminant Analysis. Journal of Machine Learning Research. 5. 121–128.4 indexed citations
9.
Shawe‐Taylor, John & David R. Hardoon. (2009). PAC-Bayes Analysis Of Maximum Entropy Classification. UCL Discovery (University College London). 5. 480–487.6 indexed citations
Szedmák, Sándor, Tijl De Bie, & David R. Hardoon. (2007). A metamorphosis of Canonical Correlation Analysis into Multivariate Maximum Margin Learning. Ghent University Academic Bibliography (Ghent University). 211–216.11 indexed citations
13.
Hardoon, David R., et al.. (2007). Information Retrieval by Inferring Implicit Queries from Eye Movements. International Conference on Artificial Intelligence and Statistics. 179–186.22 indexed citations
Farquhar, Jason, David R. Hardoon, Hongying Meng, John Shawe‐Taylor, & Sándor Szedmák. (2005). Two view learning: SVM-2K, Theory and Practice. Brunel University Research Archive (BURA) (Brunel University London). 18. 355–362.246 indexed citations
16.
Hardoon, David R. & Larry M. Manevitz. (2005). fMRI analysis via one-class machine learning techniques. International Joint Conference on Artificial Intelligence. 1604–1605.17 indexed citations
17.
Hardoon, David R. & Larry M. Manevitz. (2005). One-class Machine Learning Approach for fMRI Analysis. ePrints Soton (University of Southampton).5 indexed citations
18.
Hardoon, David R., John Shawe‐Taylor, & Ola Friman. (2004). KCCA Feature Selection for fMRI Analysis. ePrints Soton (University of Southampton).2 indexed citations
Hardoon, David R. & John Shawe‐Taylor. (2003). Signal Extraction for Brain-Computer Interface. ePrints Soton (University of Southampton).2 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.