Publications by topic: 
The FastICA algorithm[This is probably the most widely used algorithm for performing independent component analysis, a recently developed variant of factor analysis that is completely estimable unlike classical methods, and able to perform blind source separation]FastICA package for Matlab and other systems
A. Hyvärinen.
Fast and Robust FixedPoint Algorithms for Independent Component Analysis. IEEE Transactions on Neural Networks 10(3):626634, 1999.
A. Hyvärinen and E. Oja.
Independent Component Analysis: Algorithms and Applications.
Neural Networks, 13(45):411430, 2000.
A. Hyvärinen and E. Oja. A Fast FixedPoint Algorithm for Independent
Component Analysis.
Neural Computation, 9(7):14831492, 1997.
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A. Hyvärinen. New Approximations of Differential Entropy
for Independent Component Analysis and Projection Pursuit. In
Advances in Neural Information Processing Systems 10 (NIPS*97), pp. 273279, MIT Press, 1998.
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E. Bingham and A. Hyvärinen
A fast fixedpoint algorithm for independent component analysis of complexvalued signals.
Int. J. of Neural Systems, 10(1):18, 2000.
A. Hyvärinen. OneUnit Contrast Functions for Independent
Component Analysis: A Statistical Analysis. In Neural Networks
for Signal Processing VII (Proc. IEEE NNSP Workshop '97, Amelia Island,
Florida), pp. 388397, 1997.
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A. Hyvärinen. The FixedPoint Algorithm and Maximum Likelihood Estimation for Independent Component Analysis. Neural Processing Letters, 10(1):15.
A. Hyvärinen. Gaussian Moments for Noisy Independent Component Analysis.
A. Hyvärinen and U. Köster. FastISA: A fast fixedpoint algorithm for independent subspace analysis
