Abstract: Unsupervised learning is a classical approach in pattern recognition and data analysis. Its importance is growing today, due to the increasing data volumes and the difficulty of obtaining statistically sufficient amounts of labelled training data. Typical analysis techniques using unsupervised learning are principal component analysis, independent component analysis, and cluster analysis. They can all be presented as decompositions of the data matrix containing the unlabeled samples. Starting from the classical results, the author reviews some advances in the field up to the present day.

Speaker: Erkki Oja

Affiliation: Professor Emeritus, Aalto University

Place of Seminar: Aalto University