Mika Inki

548 total citations
6 papers, 330 citations indexed

About

Mika Inki is a scholar working on Signal Processing, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Mika Inki has authored 6 papers receiving a total of 330 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Signal Processing, 5 papers in Artificial Intelligence and 2 papers in Computer Vision and Pattern Recognition. Recurrent topics in Mika Inki's work include Blind Source Separation Techniques (6 papers), Neural Networks and Applications (5 papers) and Image and Signal Denoising Methods (2 papers). Mika Inki is often cited by papers focused on Blind Source Separation Techniques (6 papers), Neural Networks and Applications (5 papers) and Image and Signal Denoising Methods (2 papers). Mika Inki collaborates with scholars based in Finland. Mika Inki's co-authors include Aapo Hyvärinen and Patrik O. Hoyer and has published in prestigious journals such as Neural Computation, Journal of Mathematical Imaging and Vision and Aaltodoc (Aalto University).

In The Last Decade

Mika Inki

6 papers receiving 295 citations

Peers

Mika Inki
T. Adali United States
R. Moddemeijer Netherlands
Geng-Shen Fu United States
Matthew Anderson United States
P. Pajunen Finland
Te-Won Lee United States
T. Adali United States
Mika Inki
Citations per year, relative to Mika Inki Mika Inki (= 1×) peers T. Adali

Countries citing papers authored by Mika Inki

Since Specialization
Citations

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

Fields of papers citing papers by Mika Inki

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mika Inki

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

All Works

6 of 6 papers shown
1.
Inki, Mika. (2004). EXTENSIONS OF INDEPENDENT COMPONENT ANALYSIS FOR NATURAL IMAGE DATA. Aaltodoc (Aalto University). 5 indexed citations
2.
Inki, Mika & Aapo Hyvärinen. (2003). Two approaches to estimation of overcomplete independent component bases. 11. 454–459. 1 indexed citations
3.
Hyvärinen, Aapo & Mika Inki. (2002). Estimating Overcomplete Independent Component Bases for Image Windows. Journal of Mathematical Imaging and Vision. 17(2). 139–152. 20 indexed citations
4.
Hyvärinen, Aapo, Patrik O. Hoyer, & Mika Inki. (2001). Topographic Independent Component Analysis. Neural Computation. 13(7). 1527–1558. 293 indexed citations
5.
Inki, Mika & Aapo Hyvärinen. (2001). Two Methods for Estimating Overcomplete Independent Component Bases. 6 indexed citations
6.
Hyvärinen, Aapo, Patrik O. Hoyer, & Mika Inki. (2000). Topographic ICA as a model of V1 receptive fields. 11. 83–88 vol.4. 5 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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