Ensemble Learning for Blind Image Separation and De onvolution

Nonlinear Static and Dynamic Blind Source Separation Using Ensemble Learning

An Ensemble Learning Approach to Nonlinear Dynamic Blind Source Separation Using State-Space Models

We propose a new method for learning a nonlinear dynamical state-space model in unsupervised manner. The proposed method can be viewed as a nonlinear dynamic generalization of standard linear blind source separation (BSS) or independent component analysis (ICA). Using ensemble learning, the method finds a nonlinear dynamical process which can explain the observations. The nonlinearities are mod...

Theoretical background for ensemble methods with multivariate decomposition

In this paper we present the theoretical background for destructive component identification in ensemble method via multivariate decompositions. The identification method is based on second order statistics and it is addressed for predictive models scored by MSE criterion. The validity of the concept is confirmed by simulation study based on Friedman benchmark function. Key-Words: Ensemble meth...

Nonlinear Blind Source Separation by Variational Bayesian Learning

Blind separation of sources from their linear mixtures is a well understood problem. However, if the mixtures are nonlinear, this problem becomes generally very difficult. This is because both the nonlinear mapping and the underlying sources must be learned from the data in a blind manner, and the problem is highly ill-posed without a suitable regularization. In our approach, multilayer percept...

Blind Signal Separation Using an Extended Infomax Algorithm

The Infomax algorithm is a popular method in blind source separation problem. In this article an extension of the Infomax algorithm is proposed that is able to separate mixed signals with any sub- or super-Gaussian distributions. This ability is the results of using two different nonlinear functions and new coefficients in the learning rule. In this paper we show how we can use the distribution...