Semi-supervised multi-label feature sélection

From top to bottom: initial labels, segmentation results with a gradient-based potential function [12], segmentation results with a color and region-based potential function

The supervised classifier must automatically find the most discriminating feature set for any set of images and any number of classes because the users are not specialists in

Figure 2 and Figure 3 further show that semi- supervised training using label propagation on transformed representations (‘h1’ and ‘h2’) per- forms better than the ‘Baseline’

In practice, popular SSL methods as Standard Laplacian (SL), Normalized Laplacian (NL) or Page Rank (PR), exploit those operators defined on graphs to spread the labels and, from

Focusing on these two settings, we introduce a new algorithm that leverages the manifold structure of the labeled and unlabeled data distribution to predict pseudo-labels,

• We provide supervised and semi-supervised estimation procedures, which are optimal or optimal up to an extra logarithmic factor. Importantly, our results show that

In this work, we employ a transductive label propagation method that is based on the manifold assumption to make predictions on the entire dataset and use these predictions to

More specifically, these applications relate to: (a) exposure, as CDRs allow for a precise spatial modelling of both population density and human mobility in cities at a