Aapo Hyvärinen mostly deals with Independent component analysis, Artificial intelligence, Pattern recognition, Blind signal separation and Applied mathematics. His work deals with themes such as Data mining, Functional magnetic resonance imaging, Neural coding, Algorithm and Principal component analysis, which intersect with Independent component analysis. His study in Neural coding is interdisciplinary in nature, drawing from both Artificial neural network, Probabilistic logic, Receptive field and Visual cortex.
The study incorporates disciplines such as Estimator and Projection pursuit in addition to Algorithm. Aapo Hyvärinen has researched Artificial intelligence in several fields, including Machine learning and Computer vision. His Feature extraction study, which is part of a larger body of work in Pattern recognition, is frequently linked to Noise, bridging the gap between disciplines.
Aapo Hyvärinen focuses on Artificial intelligence, Independent component analysis, Pattern recognition, Algorithm and Nonlinear system. His studies in Artificial intelligence integrate themes in fields like Machine learning and Computer vision. Aapo Hyvärinen connects Independent component analysis with Blind signal separation in his study.
His Pattern recognition research incorporates elements of Image processing, Maximization and Visual cortex. The concepts of his Algorithm study are interwoven with issues in Estimator, Representation, Probabilistic logic and Cluster analysis. His Probabilistic logic study combines topics from a wide range of disciplines, such as Quadratic equation and Jacobian matrix and determinant.
His scientific interests lie mostly in Algorithm, Artificial intelligence, Nonlinear system, Artificial neural network and Identifiability. His Algorithm study integrates concerns from other disciplines, such as Probabilistic logic, Parametric statistics, Normalizing constant and Cluster analysis. His work carried out in the field of Artificial intelligence brings together such families of science as Data modeling, Machine learning and Pattern recognition.
As a part of the same scientific family, Aapo Hyvärinen mostly works in the field of Pattern recognition, focusing on Functional magnetic resonance imaging and, on occasion, Video based and Information processing. His work in Artificial neural network addresses subjects such as Maximum likelihood, which are connected to disciplines such as Autoregressive model. His biological study spans a wide range of topics, including Working memory, Noise and Extension.
Aapo Hyvärinen spends much of his time researching Nonlinear system, Algorithm, Identifiability, Latent variable and Feature learning. Nonlinear system combines with fields such as Applied mathematics, Variable, Independent component analysis, Multivariate statistics and Bivariate analysis in his work. His Independent component analysis research includes themes of Subspace topology, Pooling, Measure, Current and Simple.
His Algorithm study incorporates themes from Stochastic gradient descent, Curse of dimensionality, Stability, Generative model and Multilayer perceptron. His research on Identifiability also deals with topics like
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.
Independent component analysis: algorithms and applications
A. Hyvärinen;E. Oja.
Neural Networks (2000)
Independent Component Analysis
Aapo Hyvarinen;Juha Karhunen;Erkki Oja.
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks (1999)
A fast fixed-point algorithm for independent component analysis
Aapo Hyvärinen;Erkki Oja.
Neural Computation (1997)
Survey on Independent Component Analysis
Neural Computing Surveys (1999)
Validating the independent components of neuroimaging time series via clustering and visualization.
Johan Himberg;Aapo Hyvärinen;Fabrizio Esposito.
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Michael Gutmann;Aapo Hyvärinen.
international conference on artificial intelligence and statistics (2010)
A Linear Non-Gaussian Acyclic Model for Causal Discovery
Shohei Shimizu;Patrik O. Hoyer;Aapo Hyvärinen;Antti Kerminen.
Journal of Machine Learning Research (2006)
A fast fixed-point algorithm for independent component analysis of complex valued signals.
Ella Bingham;Aapo Hyvärinen.
International Journal of Neural Systems (2000)
Emergence of Phase- and Shift-Invariant Features by Decomposition of Natural Images into Independent Feature Subspaces
Aapo Hyvärinen;Patrik Hoyer.
Neural Computation (2000)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: