Abstract — Denoising source separation (DSS), a recently developed source separation framework, was applied to extracting components exhibiting slow, interannual temporal behaviour from climate data. Three datasets with daily measurements were used: surface temperature, sea level pressure and precipitation around the globe. For all datasets, the first extracted component captured the well-known El Niño–Southern Oscillation phenomenon and the second component was close to the derivative of the first one. Several other components with slow dynamics were extracted and together the components appear to capture essential features of the slow-dynamics state of the climate system. The first two components were identified reliably but the following components may have remained mixed. Nonlinear DSS could identify the physically most meaningful rotation among them but only linear DSS was within the scope of this paper. This paper offers a simple demonstration of exploratory data analysis of climate data by DSS and suggests future lines of research. I.

Abstract. The denoising source separation framework is extended to nonlinear separation of image mixtures. MLP networks are used to model the nonlinear unmixing mapping. Learning is guided by a denoising function which uses prior knowledge about the sparsity of the edges in images. The main benefit of the method is that it is simple and computationally efficient. Separation results on a real-world image mixture proved to be comparable to those achieved with MISEP. 1

Abstract. The paper presents an algorithm for identifying the independent subspace analysis model based on source dynamics. We propose to separate subspaces by decoupling their dynamic models. Each subspace is extracted by minimizing the prediction error given by a first-order nonlinear autoregressive model. The learning rules are derived from a cost function and implemented in the framework of denoising source separation. 1

In this paper, we review the recently proposed denoising source separation (DSS) framework. In the DSS framework, source separation algorithms are constructed around denoising proceduces. The denoising should reflect the prior knowledge of the source characteristics and it can be procedural. Source separation methods are an active research topic in signal processing domain but they can also be applied to feature extraction. It has also been proposed that independent component analysis (ICA) and related methods have similarities with sensory processing in the brain. The main purpose of this paper is to discuss extensions to the basic DSS framework to make it more suited for feature extraction. We also discuss connections with sensory processing in the brain. 1

We present a probabilistic factor analysis model which can be used for studying spatio-temporal datasets. The spatial and temporal structure is modeled by using Gaussian process priors both for the loading matrix and the factors. The posterior distributions are approximated using the variational Bayesian framework. High computational cost of Gaussian process modeling is reduced by using sparse approximations. The model is used to compute the reconstructions of the global sea surface temperatures from a historical dataset. The results suggest that the proposed model can outperform the state-of-the-art reconstruction systems. 1

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maximum likelihood algorithm for slow features analysis

Abstract. We discuss a method to extract independent dynamical systems underlying a single or multiple channels of observation. In particular, we search for one dimensional subsignals to aid the interpretability of the decomposition. The method uses an approximate Bayesian analysis to determine automatically the number and appropriate complexity of the underlying dynamics, with a preference for the simplest solution. We apply this method to unfiltered EEG signals to discover low complexity sources with preferential spectral properties, demonstrating improved interpretability of the extracted sources over related methods. 2 IDIAP–RR 05-84 1

Abstract — We discuss a method to extract independent dynamical systems underlying a single or multiple channels of observation. In particular, we search for one dimensional subsignals to aid the interpretability of the decomposition. The method uses an approximate Bayesian analysis to determine automatically the number and appropriate complexity of the underlying dynamics, with a preference for the simplest solution. We apply this method to unfiltered EEG signals to discover low complexity sources with preferential spectral properties, demonstrating improved interpretability of the extracted sources over related methods. I.

What is Blind and Semi-blind Source Separation? Blind source separation (BSS) is a class of computational data analysis techniques for revealing hidden factors, that underlie sets of measurements or signals. BSS assumes a statistical model whereby the