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(45):411430, 2000.
A. Hyvärinen, J. Karhunen and E. Oja.
Independent Component Analysis.
A. Hyvärinen. Survey on Independent Component Analysis.
Neural Computing Surveys 2:94128, 1999.
Validation and testing
A. Hyvärinen.
Testing the ICA mixing matrix based on intersubject or intersession consistency. NeuroImage, 58:122136, 2011.
A. Hyvärinen and P. Ramkumar.
Testing independent component patterns by intersubject or intersession consistency, Frontiers in Human Neuroscience, 7:94, 2013.
J. Himberg, A. Hyvärinen and F. Esposito. Validating the independent components of neuroimaging timeseries via clustering and visualization.
NeuroImage 22(3):12141222, 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.
Dependent components
H. Sasaki, M. U. Gutmann, H. Shouno and A. Hyvärinen.
Correlated Topographic Analysis: Estimating an Ordering of Correlated Components. Machine Learning, 92:285317, 2013.
A. Hyvärinen and S. Shimizu.
A quasistochastic gradient algorithm for variancedependent component analysis.
In Proc. International Conference on Artificial Neural Networks (ICANN2006), Athens, Greece, pp. 211220, 2006.
A. Hyvärinen, P.O. Hoyer and M. Inki. Topographic Independent
Component Analysis. Neural Computation, 13(7):15271558, 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 Nonlinear ICAA. Hyvärinen and P. Pajunen. Nonlinear Independent Component Analysis:
Existence and Uniqueness results. Neural Networks 12(3): 429439, 1999.
P. Pajunen, A. Hyvärinen and J. Karhunen. NonLinear Blind Source
Separation by SelfOrganizing Maps. In Proc. Int. Conf. on Neural
Information Processing, Hong Kong, pp. 12071210, 1996.
Noisy data and denoising
A. Hyvärinen. Sparse Code Shrinkage: Denoising of Nongaussian
Data by Maximum Likelihood Estimation.
Neural Computation, 11(7):17391768, 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,
22:4967, 1998.
(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 nonGaussian statespace 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 shorttime Fourier transforms for spontaneous EEG/MEG analysis.
NeuroImage, 49(1):257271, 2010.
A. Hyvärinen.
A unifying model for blind separation of independent sources.
Signal Processing, 85(7):14191427, 2005.
A. Hyvärinen and J. Hurri.
Blind separation of sources that have spatiotemporal dependencies.
Signal Processing, 84(2):247254, 2004 (special issue on nonlinear and nonindependent source separation).
A. Hyvärinen.
Complexity Pursuit: Separating interesting components from timeseries.
Neural Computation, 13(4):883898, 2001.
A. Hyvärinen.
Blind source separation by nonstationarity of variance: A cumulantbased approach.
IEEE Trans. on Neural Networks, 12(6):14711474, 2001.
A. Hyvärinen. Independent Component Analysis
for Timedependent Stochastic Processes. In Proc. Int. Conf. on Artificial Neural Networks (ICANN'98),
Skövde, Sweden, pp. 541546, 1998.
Other theoretical topics
J. Puuronen and A. Hyvärinen.
Hermite Polynomials and Measures of NonGaussianity
.
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, 49:151162, 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. 425429, Aussois, France, 1999.
A. Hyvärinen and E. Bingham.
Connection between multilayer perceptrons and regression using independent component analysis. Neurocomputing, 50(C):211222, 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, 17:139152, 2002.
A. Hyvärinen and E. Oja. Independent Component Analysis by General
Nonlinear Hebbianlike Learning Rules. Signal Processing,
64(3):301313, 1998.
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.
Applications
T. Honkela, A. Hyvärinen, and J. Väyrynen
WordICA  Emergence of Feature Representations for Words by Independent Component Analysis.
Natural Language Engineering, 16(3):277308, 2010.
J. Perkiö and A. Hyvärinen.
Modelling image complexity by independent component analysis, with application to contentbased 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, 50:4759, 2014.
