2022 - Research.com Computer Science in Finland Leader Award
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
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 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.
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.
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.
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Independent component analysis: algorithms and applications
A. Hyvärinen;E. Oja.
Neural Networks (2000)
Independent Component Analysis
Aapo Hyvarinen;Juha Karhunen;Erkki Oja.
A fast fixed-point algorithm for independent component analysis
Aapo Hyvärinen;Erkki Oja.
Neural Computation (1997)
Simplified neuron model as a principal component analyzer
Journal of Mathematical Biology (1982)
A new curve detection method: randomized Hough transform (RHT)
L. Xu;E. Oja;P. Kultanen.
Pattern Recognition Letters (1990)
Subspace methods of pattern recognition
NEURAL NETWORKS, PRINCIPAL COMPONENTS, AND SUBSPACES
International Journal of Neural Systems (1989)
Original Contribution: Principal components, minor components, and linear neural networks
Neural Networks (1992)
Engineering applications of the self-organizing map
T. Kohonen;E. Oja;O. Simula;A. Visa.
Proceedings of the IEEE (1996)
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)
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