The DipEncoder: Enforcing Multimodality in Autoencoders

The DipEncoder: Enforcing Multimodality in Autoencoders

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.

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Authors
  • Leiber, Collin
  • Bauer, Lena G. M.
  • Neumayr, Michael
  • Plant, Claudia
  • Böhm, Christian
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Shortfacts
Category
Paper in Conference Proceedings or in Workshop Proceedings (Paper)
Event Title
KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14 - 18, 2022
Divisions
Data Mining and Machine Learning
Event Location
Washington DC, USA
Event Type
Conference
Event Dates
14-18 Aug 2022
Publisher
ACM
Page Range
pp. 846-856
Date
2022
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