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An equivalence between log-sum-exp approximation and entropy regularization in <i>K</i>-means clustering
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- Inoue Kohei
- Faculty of Design, Kyushu University
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- Hara Kenji
- Faculty of Design, Kyushu University
Description
<p>In this paper, we show an equivalence between log-sum-exp approximation and entropy regularization in K-means clustering, which is a well-known algorithm for partitional clustering. We derive an identical equation for updating centroids of clusters from the two formulations. Additionally, we derive an alternative equation suitable for another formulation of entropy regularization, maximum entropy method. We also show experimental results which support the theoretical results.</p>
Journal
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- Nonlinear Theory and Its Applications, IEICE
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Nonlinear Theory and Its Applications, IEICE 11 (4), 446-453, 2020
The Institute of Electronics, Information and Communication Engineers
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Keywords
Details 詳細情報について
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- CRID
- 1390848647560842752
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- NII Article ID
- 130007921448
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- ISSN
- 21854106
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- HANDLE
- 2324/4113192
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- Text Lang
- en
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- Article Type
- journal article
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- Data Source
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- JaLC
- IRDB
- Crossref
- CiNii Articles
- OpenAIRE
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- Abstract License Flag
- Disallowed