Recent research in automated learning has focused on algorithms that learn from a combination of tagged and untagged data. Such algorithms can be referred to as semi-supervised in...
Many clustering algorithms have been proposed to partition a set of static data points into groups. In this paper, we consider an evolutionary clustering problem where the input d...
We study a sparse coding learning algorithm that allows for a simultaneous learning of the data sparseness and the basis functions. The algorithm is derived based on a generative m...
This paper proposes a probabilistic graphical model for the problem of propagating labels in video sequences, also termed the label propagation problem. Given a limited amount of ...
Multi-view learning algorithms typically assume a complete bipartite mapping between the different views in order to exchange information during the learning process. However, ma...