09.08.2022
One paper at KDD 2022
28th ACM SIGKDD International Conference on Knowledge Discovery and Data (KDD 2022). Washington, DC, USA, 14.08.2022–18.08.2022
We are happy to announce that MCML researchers are represented with one paper at KDD 2022:
The DipEncoder: Enforcing Multimodality in Autoencoders.
KDD 2022 - 28th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Washington, DC, USA, Aug 14-18, 2022. DOI
Abstract
Hartigan’s Dip-test of unimodality gained increasing interest in unsupervised learning over the past few years. It is free from complex parameterization and does not require a distribution assumed a priori. A useful property is that the resulting Dip-values can be derived to find a projection axis that identifies multimodal structures in the data set. In this paper, we show how to apply the gradient not only with respect to the projection axis but also with respect to the data to improve the cluster structure. By tightly coupling the Dip-test with an autoencoder, we obtain an embedding that clearly separates all clusters in the data set. This method, called DipEncoder, is the basis of a novel deep clustering algorithm. Extensive experiments show that the DipEncoder is highly competitive to state-of-the-art methods.
MCML Authors
Collin Leiber
Database Systems & Data Mining
Christian Böhm
Prof. Dr.
* Former member
09.08.2022
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