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We propose a " time-biased " and a " space-biased " method for spatiotemporal independent component analysis (ICA). The methods rely on computing an orthogonal approximate joint diagonalizer of a collection of covariance-like matrices. In the time-biased version, the time signatures of the ICA modes are imposed to be white, whereas the space-biased version… (More)
A data mining methodology, the random forests, is applied to analyze pollutant emission from the recovery boiler of a Kraft pulping process. Starting from a large database of raw process data, the goal is to identify the input variables that explain the most output variations.