Original papers

The following eight publications describe the development of methods suitable for unsupervised learning of nonlinear factor analysis models.

I

H. Lappalainen, ``Using an MDL-based cost function with neural networks,'' in Proceedings of the International Joint Conference on Neural Networks, IJCNN'98, (Anchorage, Alaska, USA, May 4-9), pp. 2384-2389, 1998.

II

H. Lappalainen, ``Ensemble Learning for Independent Component Analysis,'' in Proceedings of the First International Workshop on Independent Component Analysis and Blind Signal Separation, ICA'99, (Aussois, France, Jan. 11-15), pp. 7-12, 1999.

III

H. Lappalainen and X. Giannakopoulos, ``Multi-Layer Perceptrons as Nonlinear Generative Models for Unsupervised Learning: a Bayesian Treatment,'' in Proceedings of the Ninth International Conference on Artificial Neural Networks, ICANN'99, (Edinburgh, UK, Sep. 7-10), pp. 19-24, 1999.

IV

H. Lappalainen and J. W. Miskin, ``Ensemble Learning,'' in Advances in Independent Component Analysis (M. Girolami, ed.), pp. 76-92, Springer-Verlag, 2000.

V

H. Lappalainen and A. Honkela, ``Bayesian Nonlinear Independent Component Analysis by Multi-Layer Perceptrons,'' in Advances in Independent Component Analysis (M. Girolami, ed.), pp. 93-121, Springer-Verlag, 2000.

VI

H. Valpola, ``Nonlinear Independent Component Analysis Using Ensemble Learning: Theory,'' in Proceedings of the Second International Workshop on Independent Component Analysis and Blind Signal Separation, ICA 2000, (Helsinki, Finland, June 19-22), pp. 251-256, 2000.

VII

H. Valpola and P. Pajunen, ``Fast Algorithms for Bayesian Independent Component Analysis,'' in Proceedings of the Second International Workshop on Independent Component Analysis and Blind Signal Separation, ICA 2000, (Helsinki, Finland, June 19-22), pp. 233-237, 2000.

VIII

H. Valpola, ``Unsupervised learning of nonlinear dynamic state-space models,'' Publications in Computer and Information Science A59, Helsinki University of Technology, Espoo, Finland, 2000.