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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence

Deep Embedded Non-Redundant Clustering

February 1, 2023

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Authors

Lukas Miklautz

University of Vienna


Dominik Mautz

Ludwig-Maximilians-Universität München


Muzaffer Can Altinigneli

Ludwig-Maximilians-Universität München


Christian Böhm

Ludwig-Maximilians-Universität München


Claudia Plant

University of Vienna


DOI:

10.1609/aaai.v34i04.5961


Abstract:

Complex data types like images can be clustered in multiple valid ways. Non-redundant clustering aims at extracting those meaningful groupings by discouraging redundancy between clusterings. Unfortunately, clustering images in pixel space directly has been shown to work unsatisfactory. This has increased interest in combining the high representational power of deep learning with clustering, termed deep clustering. Algorithms of this type combine the non-linear embedding of an autoencoder with a clustering objective and optimize both simultaneously. None of these algorithms try to find multiple non-redundant clusterings. In this paper, we propose 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.

Topics: AAAI

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HOW TO CITE:

Lukas Miklautz||Dominik Mautz||Muzaffer Can Altinigneli||Christian Böhm||Claudia Plant Deep Embedded Non-Redundant Clustering Proceedings of the AAAI Conference on Artificial Intelligence (2020) 5174-5181.

Lukas Miklautz||Dominik Mautz||Muzaffer Can Altinigneli||Christian Böhm||Claudia Plant Deep Embedded Non-Redundant Clustering AAAI 2020, 5174-5181.

Lukas Miklautz||Dominik Mautz||Muzaffer Can Altinigneli||Christian Böhm||Claudia Plant (2020). Deep Embedded Non-Redundant Clustering. Proceedings of the AAAI Conference on Artificial Intelligence, 5174-5181.

Lukas Miklautz||Dominik Mautz||Muzaffer Can Altinigneli||Christian Böhm||Claudia Plant. Deep Embedded Non-Redundant Clustering. Proceedings of the AAAI Conference on Artificial Intelligence 2020 p.5174-5181.

Lukas Miklautz||Dominik Mautz||Muzaffer Can Altinigneli||Christian Böhm||Claudia Plant. 2020. Deep Embedded Non-Redundant Clustering. "Proceedings of the AAAI Conference on Artificial Intelligence". 5174-5181.

Lukas Miklautz||Dominik Mautz||Muzaffer Can Altinigneli||Christian Böhm||Claudia Plant. (2020) "Deep Embedded Non-Redundant Clustering", Proceedings of the AAAI Conference on Artificial Intelligence, p.5174-5181

Lukas Miklautz||Dominik Mautz||Muzaffer Can Altinigneli||Christian Böhm||Claudia Plant, "Deep Embedded Non-Redundant Clustering", AAAI, p.5174-5181, 2020.

Lukas Miklautz||Dominik Mautz||Muzaffer Can Altinigneli||Christian Böhm||Claudia Plant. "Deep Embedded Non-Redundant Clustering". Proceedings of the AAAI Conference on Artificial Intelligence, 2020, p.5174-5181.

Lukas Miklautz||Dominik Mautz||Muzaffer Can Altinigneli||Christian Böhm||Claudia Plant. "Deep Embedded Non-Redundant Clustering". Proceedings of the AAAI Conference on Artificial Intelligence, (2020): 5174-5181.

Lukas Miklautz||Dominik Mautz||Muzaffer Can Altinigneli||Christian Böhm||Claudia Plant. Deep Embedded Non-Redundant Clustering. AAAI[Internet]. 2020[cited 2023]; 5174-5181.


ISSN: 2374-3468


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