Multiple Output Regression with Latent Noise

no code implementations • 27 Oct 2014 • Jussi Gillberg, Pekka Marttinen, Matti Pirinen, Antti J. Kangas, Pasi Soininen, Mehreen Ali, Aki S. Havulinna, Marjo-Riitta Marjo-Riitta Järvelin, Mika Ala-Korpela, Samuel Kaski

In high-dimensional data, structured noise caused by observed and unobserved factors affecting multiple target variables simultaneously, imposes a serious challenge for modeling, by masking the often weak signal.

regression Time Series +1

Bayesian Information Sharing Between Noise And Regression Models Improves Prediction of Weak Effects

no code implementations • 16 Oct 2013 • Jussi Gillberg, Pekka Marttinen, Matti Pirinen, Antti J. Kangas, Pasi Soininen, Marjo-Riitta Järvelin, Mika Ala-Korpela, Samuel Kaski

To facilitate the prediction of the weak effects, we constrain our model structure by introducing a novel Bayesian approach of sharing information between the regression model and the noise model.

regression