Peter Berger

2 accepted papers

2019

Semi-supervised Multiclass Clustering Based on Signed Total Variation

Peter Berger, Thomas Dittrich, Gabor Hannak, Gerald Matz

ICASSP 2019accepted

We consider the problem of semi-supervised clustering for multiple (more than two) classes. The proposed clustering algorithm uses the (dis)similarity of given data to learn the unknown cluster labels. We quantify label (dis)similarity in terms of the new concept of signed total variation (TV). The…

Cited by 0 Source ↗Scholar ↗

2018

Graph Learning Based on Total Variation Minimization

Peter Berger, Manfred Buchacher, Gabor Hannak, Gerald Matz

ICASSP 2018accepted

We consider the problem of learning the topology of a graph from a given set of smooth graph signals. We construct a weighted adjacency matrix that best explains the data in the sense of achieving the smallest graph total variation. For the case of noisy measurements of the graph signals we propose…

Cited by 0 Source ↗Scholar ↗