Unsupervised learning is a branch of machine learning that aims to uncover the underlying structures in the data. A common task in unsupervised learning is clustering, where the goal is to group a set of objects into clusters such that object in the same group are more similar to each other than to those in another group. Traditionally, systems based on machine learning have fared well in cases where each data sample is accompanied by a corresponding label. But obtaining labels can be challenging in a number of different domains. One example is machine learning for medical applications, where vast amounts of patient data are being gathered. However, labeling such data requires significant domain knowledge and can be time-consuming. Therefore, developing algorithms that can take advantage of unlabeled data is an important research direction.
The UiT Machine Learning Group has focused on employing kernel methods and deep learning methodology in order to forward unsupervised learning research. For instance, by combining deep neural networks with information theoretic concepts, our group has developed a novel approach to clustering that encourages separation between clusters and compactness within the clusters.
- F. M. Bianchi, L. Livi, K.Ø, Mikalsen, M. Kampffmeyer and R. Jenssen
- Daniel J. Trosten, Andreas S. Strauman, Michael Kampffmeyer, Robert Jenssen