We are proud to announce that our paper "Deep Embedded Non-Redundant Clustering" by Lukas Miklautz* (University of Vienna), Dominik Mautz* (Ludwig-Maximilians-Universität), Can Altinigneli (University of Vienna & Ludwig-Maximilians-Universität), Christian Böhm (Ludwig-Maximilians-Universität), and Claudia Plant (University of Vienna) has been accepted at the valuable AAAI conference.
We proposed the novel Embedded Non-Redundant Clustering algorithm (ENRC). It is the first algorithm that combines neural-network-based representation learning with non-redundant clustering. ENRC can find multiple highly non-redundant clusterings of different dimensionalities within a data set. This is achieved by (softly) assigning each dimension of the embedded space to the different clusterings. For instance, in image data sets it can group the objects by color, material and shape, without the need for explicit feature engineering. We show the viability of ENRC in extensive experiments and empirically demonstrate the advantage of combining non-linear representation learning with non-redundant clustering.
* First authors with equal contribution.