Department of Computer Science

Abstract: E-business frameworks providing standards and specifications that aim at enabling integrating the enterprise applications of business partners with relative ease have been recently proposed. This paper reports on experiences gained from developing a prototype ...

Samuel Kaski (chair) David J. Miller (chair) ... Tülay Adali Jerónimo Arenas-Garcıa Amir Assadi Jaakko Astola Massoud Babaie-Zadeh Chengpeng Bi Adrian Bors Wray Buntine Vince Calhoun Gustavo Camps-Valls Stephane Canu Rodrigo Capobianco Guido Luis Castedo Ribas Jocelyn Chanussot Seungjin Choi Andrzej Cichocki Jukka Corander Florence d'Alche Patrick De Maziere Konstantinos Diamantaras Deniz Erdogmus Mathieu Fauvel Mark Girolami Ling Guan Janne Heikkilä Kenneth Hild II Antti Honkela Peter Hung Robert Jenssen Christian Jutten Juha ...

Abstract—In this paper, we briefly review recent advances in blind source separation (BSS) for nonlinear mixing models. After a general introduction to the nonlinear BSS and ICA (indepen-dent Component Analysis) problems, we discuss in more detail uniqueness ...

In this paper, we review recent advances in blind source separation (BSS) and independent component analysis (ICA) for nonlinear mixing models. After a general introduction to BSS and ICA, we discuss in more detail uniqueness and separability issues, presenting some new results. A fundamental difficulty in the nonlinear BSS problem and even more so in the nonlinear ICA problem is that they provide non-unique solutions without extra constraints, which are often implemented by using a suitable regularization. In this paper, we explore two possible approaches. The first one is based on structural constraints. Especially, post-nonlinear mixtures are an important special case, where a nonlinearity is applied to linear mixtures. For such mixtures, the ambiguities are essentially the same as for the linear ICA or BSS problems. The second approach uses Bayesian inference methods for estimating the best statistical parameters, under almost unconstrained models in which priors can be easily a...

ABSTRACT Variational Bayesian (VB) methods are typically only applied to models in the conjugate-exponential family using the variational Bayesian expectation maximisation (VB EM) algorithm or one of its variants. In this paper we present an efficient algorithm for applying VB to more general models. The method is based on specifying the functional form of the approximation, such as multivariate Gaussian. The parameters of the approximation are optimised using a conjugate gradient algorithm that utilises the Riemannian geometry of the space of the approximations. This leads to a very efficient algorithm for suitably structured approximations. It is shown empirically that the proposed method is comparable or superior in efficiency to the VB EM in a case where both are applicable. We also apply the algorithm to learning a nonlinear state-space model and a nonlinear factor analysis model for which the VB EM is not applicable. For these models, the proposed algorithm outperforms alternative gradient-based methods by a significant margin.

... Markus Harva, Tapani Raiko, Antti Honkela, Harri Valpola, and Juha Karhunen Neural Networks Research Centre Helsinki University of Technology PO Box 5400, FI-02015 TKK, Espoo, Finland http://www ... a handy conjugate prior (Gelman et al., 1995) for the likelihood available ...