H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 31 Citations 13,968 102 World Ranking 7881 National Ranking 68

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Machine learning

Juha Karhunen focuses on Artificial intelligence, Independent component analysis, Unsupervised learning, Blind signal separation and Artificial neural network. His work deals with themes such as Algorithm, Machine learning and Pattern recognition, which intersect with Artificial intelligence. The concepts of his Pattern recognition study are interwoven with issues in Infomax, Theoretical computer science and Neural algorithms.

His Independent component analysis study frequently draws parallels with other fields, such as Prior probability. His studies in Prior probability integrate themes in fields like Probabilistic logic, FastICA, Neural coding and Negentropy. His Hebbian theory and Learning rule study in the realm of Artificial neural network interacts with subjects such as Generalized Hebbian Algorithm.

His most cited work include:

  • Independent Component Analysis (8216 citations)
  • On stochastic approximation of the eigenvectors and eigenvalues of the expectation of a random matrix (482 citations)
  • A class of neural networks for independent component analysis (362 citations)

What are the main themes of his work throughout his whole career to date?

Juha Karhunen spends much of his time researching Artificial intelligence, Pattern recognition, Independent component analysis, Artificial neural network and Algorithm. His research on Artificial intelligence often connects related areas such as Machine learning. The concepts of his Pattern recognition study are interwoven with issues in Subspace topology, Missing data and Probability distribution.

His work carried out in the field of Independent component analysis brings together such families of science as Image processing, Canonical correlation, Singular value decomposition and Blind signal separation. His Artificial neural network study combines topics from a wide range of disciplines, such as Remote sensing and Radiance. His study on Algorithm also encompasses disciplines like

  • State space that connect with fields like Multilayer perceptron,
  • Bayesian probability, which have a strong connection to Probabilistic logic.

He most often published in these fields:

  • Artificial intelligence (58.48%)
  • Pattern recognition (38.01%)
  • Independent component analysis (26.32%)

What were the highlights of his more recent work (between 2012-2021)?

  • Artificial intelligence (58.48%)
  • Jet (5.26%)
  • Machine learning (19.88%)

In recent papers he was focusing on the following fields of study:

Artificial intelligence, Jet, Machine learning, Tokamak and Divertor are his primary areas of study. His research in Artificial intelligence intersects with topics in Data mining, Malware, Computer vision and Pattern recognition. Juha Karhunen interconnects Domain and Representation in the investigation of issues within Pattern recognition.

His Machine learning research incorporates elements of Inference, Bayesian inference and Complex dynamics. Juha Karhunen has researched Tokamak in several fields, including Nuclear engineering and Computational physics. His Artificial neural network research includes elements of Unsupervised learning, Meteorology and Feature learning.

Between 2012 and 2021, his most popular works were:

  • Bidirectional recurrent neural networks as generative models (50 citations)
  • Plasma-wall interaction studies within the EUROfusion consortium: progress on plasma-facing components development and qualification (41 citations)
  • Arbitrary Category Classification of Websites Based on Image Content (19 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Statistics
  • Machine learning

Juha Karhunen focuses on Artificial intelligence, Machine learning, Pattern recognition, Nuclear engineering and Divertor. His Artificial intelligence study integrates concerns from other disciplines, such as Series and Malware. He has included themes like Complex dynamics, Context, Inference and Bayesian inference in his Machine learning study.

His research on Pattern recognition focuses in particular on Canonical correlation. His Nuclear engineering research integrates issues from Electron cyclotron resonance, ASDEX Upgrade and Plasma surface interaction. His biological study spans a wide range of topics, including Robot, Extension, Blind signal separation and Generalized canonical correlation.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

Independent Component Analysis

Aapo Hyvarinen;Juha Karhunen;Erkki Oja.
(2001)

9460 Citations

On stochastic approximation of the eigenvectors and eigenvalues of the expectation of a random matrix

Erkki Oja;Juha Karhunen.
Journal of Mathematical Analysis and Applications (1985)

680 Citations

A class of neural networks for independent component analysis

J. Karhunen;E. Oja;L. Wang;R. Vigario.
IEEE Transactions on Neural Networks (1997)

625 Citations

Representation and separation of signals using nonlinear PCA type learning

Juha Karhunen;Jyrki Joutsensalo.
Neural Networks (1994)

519 Citations

Generalizations of principal component analysis, optimization problems, and neural networks

Juha Karhunen;Jyrki Joutsensalo.
Neural Networks (1995)

388 Citations

Advances in Nonlinear Blind Source Separation

Christian Jutten;Juha Karhunen.
(2003)

216 Citations

Nonlinear Blind Source Separation by Self-Organizing Maps

P. Pajunen;A. Hyvärinen;J. Karhunen.
(1996)

215 Citations

Neural approaches to independent component analysis and source separation.

Juha Karhunen.
the european symposium on artificial neural networks (1996)

210 Citations

An unsupervised ensemble learning method for nonlinear dynamic state-space models

Harri Valpola;Juha Karhunen.
Neural Computation (2002)

177 Citations

Applications of neural blind separation to signal and image processing

J. Karhunen;A. Hyvarinen;R. Vigario;J. Hurri.
international conference on acoustics, speech, and signal processing (1997)

165 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

If you think any of the details on this page are incorrect, let us know.

Contact us

Top Scientists Citing Juha Karhunen

Aapo Hyvärinen

Aapo Hyvärinen

University of Helsinki

Publications: 121

Andrzej Cichocki

Andrzej Cichocki

Skolkovo Institute of Science and Technology

Publications: 94

Shoji Makino

Shoji Makino

University of Tsukuba

Publications: 75

Shoko Araki

Shoko Araki

NTT (Japan)

Publications: 72

Hiroshi Sawada

Hiroshi Sawada

NTT (Japan)

Publications: 69

Christian Jutten

Christian Jutten

Grenoble Alpes University

Publications: 69

Fabian J. Theis

Fabian J. Theis

Technical University of Munich

Publications: 64

Vince D. Calhoun

Vince D. Calhoun

Georgia State University

Publications: 62

Erkki Oja

Erkki Oja

Aalto University

Publications: 56

Carlos G. Puntonet

Carlos G. Puntonet

University of Granada

Publications: 50

Tulay Adali

Tulay Adali

University of Maryland, Baltimore County

Publications: 43

Klaus-Robert Müller

Klaus-Robert Müller

Technical University of Berlin

Publications: 39

Terrence J. Sejnowski

Terrence J. Sejnowski

Salk Institute for Biological Studies

Publications: 39

Te-Won Lee

Te-Won Lee

Qualcomm (United Kingdom)

Publications: 38

Walter Kellermann

Walter Kellermann

University of Erlangen-Nuremberg

Publications: 37

Something went wrong. Please try again later.