H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 63 Citations 56,519 205 World Ranking 1271 National Ranking 4

Research.com Recognitions

Awards & Achievements

2019 - IEEE Frank Rosenblatt Award

2012 - Member of Academia Europaea

2006 - Neural Networks Pioneer Award, IEEE Computational Intelligence Society

2000 - IEEE Fellow For contributions to the theory and applications of artificial neural networks.

1994 - Fellow of the International Association for Pattern Recognition (IAPR) For contributions to pattern recognition and image processing and and service to the IAPR

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Machine learning

Erkki Oja mostly deals with Artificial intelligence, Independent component analysis, Algorithm, Artificial neural network and Pattern recognition. Erkki Oja focuses mostly in the field of Artificial intelligence, narrowing it down to topics relating to Machine learning and, in certain cases, Parallel computing. The Independent component analysis study combines topics in areas such as FastICA, Blind signal separation, Source separation, Magnetoencephalography and Generative model.

The concepts of his Algorithm study are interwoven with issues in Mathematical optimization, Non-negative matrix factorization, Principal component analysis and Parameter space. His biological study spans a wide range of topics, including Subspace topology, Data mining and Feed forward. Erkki Oja interconnects Histogram, Probabilistic logic and Prior probability in the investigation of issues within Pattern recognition.

His most cited work include:

  • Independent Component Analysis (8216 citations)
  • Independent component analysis: algorithms and applications (6221 citations)
  • A fast fixed-point algorithm for independent component analysis (2981 citations)

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

His primary areas of investigation include Artificial intelligence, Pattern recognition, Artificial neural network, Independent component analysis and Algorithm. His work carried out in the field of Artificial intelligence brings together such families of science as Machine learning and Computer vision. His studies deal with areas such as Subspace topology, Speech recognition and Hebbian theory as well as Pattern recognition.

His Independent component analysis research integrates issues from FastICA, Blind signal separation, Signal processing, Nonlinear system and Principal component analysis. His Blind signal separation research is multidisciplinary, incorporating elements of Latent variable, Source separation and Robustness. He focuses mostly in the field of Algorithm, narrowing it down to matters related to Mathematical optimization and, in some cases, Applied mathematics.

He most often published in these fields:

  • Artificial intelligence (58.90%)
  • Pattern recognition (35.96%)
  • Artificial neural network (22.26%)

What were the highlights of his more recent work (between 2009-2016)?

  • Non-negative matrix factorization (6.85%)
  • Artificial intelligence (58.90%)
  • Cluster analysis (8.90%)

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

Non-negative matrix factorization, Artificial intelligence, Cluster analysis, Algorithm and Nonnegative matrix are his primary areas of study. His work carried out in the field of Artificial intelligence brings together such families of science as Machine learning, Data mining and Pattern recognition. His research integrates issues of Speech recognition and Source separation in his study of Pattern recognition.

The various areas that Erkki Oja examines in his Cluster analysis study include Probabilistic latent semantic analysis, Maxima and minima, Mathematical optimization and Rank. His Algorithm research incorporates elements of Matrix decomposition and Blind signal separation. His Robustness study incorporates themes from Independent component analysis and FastICA.

Between 2009 and 2016, his most popular works were:

  • Linear and Nonlinear Projective Nonnegative Matrix Factorization (169 citations)
  • GPU-accelerated and parallelized ELM ensembles for large-scale regression (131 citations)
  • Clustering by Nonnegative Matrix Factorization Using Graph Random Walk (66 citations)

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

  • Artificial intelligence
  • Statistics
  • Machine learning

Erkki Oja spends much of his time researching Non-negative matrix factorization, Algorithm, Nonnegative matrix, Cluster analysis and Discrete mathematics. In the field of Algorithm, his study on Least squares overlaps with subjects such as Numerical weather prediction. His Nonnegative matrix research is multidisciplinary, incorporating perspectives in Kullback–Leibler divergence, Artificial intelligence, Graph partition, Norm and Applied mathematics.

His Artificial intelligence study frequently draws connections between adjacent fields such as Supercomputer. His studies in Cluster analysis integrate themes in fields like Pattern recognition, Random walk and Rank. His Pattern recognition study frequently draws connections to other fields, such as Column.

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: algorithms and applications

A. Hyvärinen;E. Oja.
Neural Networks (2000)

22048 Citations

Independent Component Analysis

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

9460 Citations

A fast fixed-point algorithm for independent component analysis

Aapo Hyvärinen;Erkki Oja.
Neural Computation (1997)

4497 Citations

Simplified neuron model as a principal component analyzer

Erkki Oja.
Journal of Mathematical Biology (1982)

2973 Citations

A new curve detection method: randomized Hough transform (RHT)

L. Xu;E. Oja;P. Kultanen.
Pattern Recognition Letters (1990)

1566 Citations

Subspace methods of pattern recognition

Erkki Oja.
(1983)

1209 Citations

NEURAL NETWORKS, PRINCIPAL COMPONENTS, AND SUBSPACES

Erkki Oja.
International Journal of Neural Systems (1989)

1168 Citations

Original Contribution: Principal components, minor components, and linear neural networks

Erkki Oja.
Neural Networks (1992)

1164 Citations

Engineering applications of the self-organizing map

T. Kohonen;E. Oja;O. Simula;A. Visa.
Proceedings of the IEEE (1996)

1103 Citations

Independent component approach to the analysis of EEG and MEG recordings

R. Vigario;J. Sarela;V. Jousmiki;M. Hamalainen.
IEEE Transactions on Biomedical Engineering (2000)

884 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.

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Top Scientists Citing Erkki Oja

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Carlos G. Puntonet

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