breast lesion segmentation in mr images is one of the most important parts of clinical diagnostic tools. pixel classification methods have been frequently used in image segmentation with two supervised and unsupervised approaches up to now. supervised segmentation methods lead to high accuracy, but they need a large amount of labeled data, which is hard, expensive, and slow to be obtained. on t...

There is a lot of attention paid to parameter setting in supervised machine learning elds. Despite ML unsupervised systems also having many parameters to set, there has not as yet been much attention focused on the subject. In this report we summarise the major types of unsupervised systems and discuss the diierent concept quality measures used in unsupervised systems. An automatic parameter se...

Unsupervised learning studies how systems can learn to represent particular input patterns in a way that reflects the statistical structure of the overall collection of input patterns. By contrast with SUPERVISED LEARNING or REINFORCEMENT LEARNING, there are no explicit target outputs or environmental evaluations associated with each input; rather the unsupervised learner brings to bear prior b...

We give a tutorial and overview of the field of unsupervised learning from the perspective of statistical modelling. Unsupervised learning can be motivated from information theoretic and Bayesian principles. We briefly review basic models in unsupervised learning, including factor analysis, PCA, mixtures of Gaussians, ICA, hidden Markov models, state-space models, and many variants and extensio...

In this thesis we try to find out if unsupervised feature learning can be used to find white matter lesions in brain-MRI scans. Results show that unsupervised feature learning algorithm performs similar to classification using regular features. In some cases it performs even better. Downside is that unsupervised feature learning is computationally more expensive.