Sebastian Mika

18 papers receiving 1.6k citations

Hit Papers

Kernel PCA and De-Noising in Feature Spaces19982026200720161998100200300400500

Peers

Sebastian Mika
Comparison fields: 5 of 142
  • Computer Vision and Pattern Recognition 634
  • Computational Theory and Mathematics 504
  • Artificial Intelligence 454
  • Molecular Biology 323
  • Materials Chemistry 193
Replace Zhen Ji with:
Zhen Ji China
Guangyong Chen China
Dao‐Qing Dai China
Simone Fiori Italy
Gerald Matz Austria
Ingo Steinwart United States
H. Park United States
Thomas Unterthiner Austria
Francisco B. Pereira Portugal
Sebastian Mika relative to Zhen Ji China Zhen Ji's profile →
Citations per field
00.5×10×14.8×
Zhen Ji · 1×
Citations per year

Countries citing papers authored by Sebastian Mika

Since Specialization
Citations

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

Fields of papers citing papers by Sebastian Mika

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sebastian Mika

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

All Works

18 of 18 papers shown
#WorkIndexed citations
1 23
2 257
3 22
4 28
5 22
6 51
7 35
8 23
9 56
10 65
11 305
12
Adapting Codes and Embeddings for Polychotomies
21
13
Barrier Boosting
25
14
A Mathematical Programming Approach to the Kernel Fisher Algorithm
116
15
Engineering Support Vector Machine Kerneis That Recognize Translation Initialion Sites.
3
16
Invariant Feature Extraction and Classification in Kernel Spaces
117
17
v-Arc: Ensemble Learning in the Presence of Outliers
8
18
Kernel PCA and De-Noising in Feature Spacesbreakdown →
535

About Sebastian Mika

Sebastian Mika is a scholar working on Computational Theory and Mathematics, Spectroscopy and Computer Vision and Pattern Recognition, having authored 18 papers that have together received 1.7k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (9 papers), Analytical Chemistry and Chromatography (6 papers) and Face and Expression Recognition (5 papers). The work is most often cited by research in Computational Theory and Mathematics (504 citations), Computer Vision and Pattern Recognition (634 citations) and Computational Mathematics (10 citations). Sebastian Mika has collaborated with scholars based in Germany, United States and Australia. Frequent co-authors include Klaus‐Robert Müller, Bernhard Schölkopf, Gunnar Rätsch, Alex Smola, Matthias Scholz, Jihun Ham, Daniel D. Lee, Nikolaus Heinrich, Timon Schroeter and Antonius ter Laak. Their work appears in journals such as Journal of Chemical Information and Modeling, Molecular Pharmaceutics and Journal of Computer-Aided Molecular Design.

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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