Patterns, clusters, and components - what data is made of

Summary form only given. Learning the implicit structure of data in large-scale applications like document and image mining or multivariate signal analysis helps in understanding the underlying causes and phenomena. The result of learning is a new explanation or compressed representation of the observation data, which lead to improved decisions. In artificial neural networks, the representation is usually a clustering of the data, a discrete map, or a lower-dimensional manifold in the observation space. The talk covered some of the paradigms of artificial neural learning based on self-organization, principal, and independent component analysis, and efficient algorithms for their computation. Many examples from the author´s research group are used to illuminate the concepts and methods.