Luigi Zanovello, Richard J G Löffler, Michele Caraglio, Thomas Franosch, Martin M Hanczyc, Pietro Faccioli
Survival strategies of artificial active agents
Scientific reports
paper novel robust principal component analysis rpca methods proposed exploit local structure datasets proposed methods derived minimizing divergence sample distribution gaussian density model divergence used different frameworks represent variants rpca approaches including orthogonal nonorthogonal sparse methods show classical pca special case proposed methods divergence reduced kullbackleibler kl divergence shown simulations proposed approaches recover underlying principal components pcs downweighting importance structured unstructured outliers furthermore using simulated data shown proposed methods applied fmri signal recovery foregroundbackground fb separation video analysis results real world problems fb separation well image reconstruction also provided