Talks and Poster Presentations (with Proceedings-Entry):
P. Berger, T. Dittrich, G. Hannak, G. Matz:
"Semi-Supervised Multiclass Clustering Based on Signed Total Variation";
Talk: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2019),
- 05-17-2019; in: "Proceedings of the 44th IEEE International Conference on Acoustics, Speech, and Signal Processing",
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 clustering task is formulated as a convex optimization problem with an l1-norm regularization term that helps when only few labels are known. We solve the optimization problem by developing an ADMM-based algorithm whose per-iteration complexity scales linearly with the number of edges and the number of clusters. Our algorithm admits a distributed implementation and can therefore efficiently handle large-dimensional problems. Numerical experiments demonstrate the superiority of our scheme.
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
Electronic version of the publication:
Created from the Publication Database of the Vienna University of Technology.