Publications by topic
Independent component analysis: misc.
Reviews on ICA[You may first want to see the what is independent component analysis page]
A Hyvärinen. Independent Component Analysis: Recent Advances.
Philosophical Transactions of the Royal Society A, 371:20110534, 2013.
A. Hyvärinen and E. Oja.
Independent Component Analysis: Algorithms and Applications.
Neural Networks, 13(4-5):411-430, 2000.
A. Hyvärinen, J. Karhunen and E. Oja.
Independent Component Analysis.
A. Hyvärinen. Survey on Independent Component Analysis.
Neural Computing Surveys 2:94--128, 1999.
Validation and testing
Testing the ICA mixing matrix based on inter-subject or inter-session consistency. NeuroImage, 58:122-136, 2011.
A. Hyvärinen and P. Ramkumar.
Testing independent component patterns by inter-subject or inter-session consistency, Frontiers in Human Neuroscience, 7:94, 2013.
J. Himberg, A. Hyvärinen and F. Esposito. Validating the independent components of neuroimaging time-series via clustering and visualization.
NeuroImage 22(3):1214-1222, 2004.
S. Shimizu, A. Hyvärinen, Y. Kano, P. O. Hoyer and A. J. Kerminen. Testing significance of mixing and demixing coefficients in ICA.
Proc. International Symposium on Independent Component Analysis and Blind Signal Separation (ICA2006), Charleston, SC, USA, 2006.
H. Sasaki, M. U. Gutmann, H. Shouno and A. Hyvärinen.
Correlated Topographic Analysis: Estimating an Ordering of Correlated Components. Machine Learning, 92:285-317, 2013.
A. Hyvärinen and S. Shimizu.
A quasi-stochastic gradient algorithm for variance-dependent component analysis.
In Proc. International Conference on Artificial Neural Networks (ICANN2006), Athens, Greece, pp. 211-220, 2006.
A. Hyvärinen, P.O. Hoyer and M. Inki. Topographic Independent
Component Analysis. Neural Computation, 13(7):1527-1558, 2001.
[Many more papers on this topic can be found in the sections on sparse coding in the visual cortex and one also here and here
A. Hyvärinen and P. Pajunen. Nonlinear Independent Component Analysis:
Existence and Uniqueness results. Neural Networks 12(3): 429--439, 1999.
P. Pajunen, A. Hyvärinen and J. Karhunen. Non-Linear Blind Source
Separation by Self-Organizing Maps. In Proc. Int. Conf. on Neural
Information Processing, Hong Kong, pp. 1207-1210, 1996.
Noisy data and denoising
A. Hyvärinen. Sparse Code Shrinkage: Denoising of Nongaussian
Data by Maximum Likelihood Estimation.
Neural Computation, 11(7):1739--1768, 1999.
A. Hyvärinen, P. Hoyer and E. Oja. Image Denoising by Sparse Code Shrinkage. In S. Haykin and B. Kosko (eds), Intelligent Signal Processing, IEEE Press, 2001.
A. Hyvärinen. Independent Component Analysis in the Presence
of Gaussian Noise by Maximizing Joint Likelihood. Neurocomputing,
(See also the FastICA section for a method based on "Gaussian Moments" for noisy ICA)
Blind separation of sources with temporal structure[As an alternative to basic ICA, these methods can be used to blindly separate sources, assuming that they have temporal correlations]
K. Zhang and A. Hyvärinen.
A general linear non-Gaussian state-space model: Identifiability, identification, and applications.Proc. Asian Conf. on Machine Learning (ACML2011), JMLR~W&CP, Taoyuan, Taiwan.
A. Hyvärinen, P. Ramkumar, L. Parkkonen, and R. Hari.
Independent component analysis of short-time Fourier transforms for spontaneous EEG/MEG analysis.
NeuroImage, 49(1):257-271, 2010.
A unifying model for blind separation of independent sources.
Signal Processing, 85(7):1419-1427, 2005.
A. Hyvärinen and J. Hurri.
Blind separation of sources that have spatiotemporal dependencies.
Signal Processing, 84(2):247-254, 2004 (special issue on nonlinear and non-independent source separation).
Complexity Pursuit: Separating interesting components from time-series.
Neural Computation, 13(4):883--898, 2001.
Blind source separation by nonstationarity of variance: A cumulant-based approach.
IEEE Trans. on Neural Networks, 12(6):1471-1474, 2001.
A. Hyvärinen. Independent Component Analysis
for Time-dependent Stochastic Processes. In Proc. Int. Conf. on Artificial Neural Networks (ICANN'98),
Skövde, Sweden, pp. 541-546, 1998.
Other theoretical topics
J. Puuronen and A. Hyvärinen.
Hermite Polynomials and Measures of Non-Gaussianity
In Proc. International Conference on Artificial Neural Networks (ICANN2011), Helsinki, Finland, 2011.
A. Hyvärinen and R. Karthikesh.
Imposing sparsity on the mixing matrix in independent component analysis.
Neurocomputing, 49:151-162, 2002 (Special Issue on ICA and BSS).
A. Hyvärinen, J. Särelä and R. Vigário.
Bumps and Spikes: Artifacts Generated by Independent Component Analysis with Insufficient Sample Size. In Proc. Int. Workshop on Independent Component Analysis and Blind Signal Separation (ICA'99), pp. 425-429, Aussois, France, 1999.
A. Hyvärinen and E. Bingham.
Connection between multi-layer perceptrons and regression using independent component analysis. Neurocomputing, 50(C):211-222, 2003.
J. Himberg and A. Hyvärinen.
Independent component analysis for binary data: An experimental study
In Proc. Int. Workshop on Independent Component Analysis and Blind Signal Separation (ICA2001), San Diego, California, 2001.
A. Hyvärinen and M. Inki.
Estimating overcomplete independent component bases for image windows.
Journal of Mathematical Imaging and Vision, 17:139-152, 2002.
A. Hyvärinen and E. Oja. Independent Component Analysis by General
Non-linear Hebbian-like Learning Rules. Signal Processing,
A. Hyvärinen. A unified probabilistic model for independent and principal component analysis. In Advances in Independent Component Analysis and Learning Machines (Festschrift to Erkki Oja), Academic Press, 2015.
T. Honkela, A. Hyvärinen, and J. Väyrynen
WordICA - Emergence of Feature Representations for Words by Independent Component Analysis.
Natural Language Engineering, 16(3):277-308, 2010.
J. Perkiö and A. Hyvärinen.
Modelling image complexity by independent component analysis, with application to content-based image retrieval. >
Proc. Int. Conf. on Artificial Neural Networks (ICANN2009), Limassol, Cyprus, 2009.
ICA and inverse modelling
J. Puuronen and A. Hyvärinen.
A Bayesian Inverse Solution using ICA.
Neural Networks, 50:47-59, 2014.