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Transformed K-means Clustering

arXiv 2111.13921 ↗First posted 27 Nov 2021cs.LG stat.MLcited by 0

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Abstract (arXiv)

In this work we propose a clustering framework based on the paradigm of transform learning. In simple terms the representation from transform learning is used for K-means clustering; however, the problem is not solved in such a na"ive piecemeal fashion. The K-means clustering loss is embedded into the transform learning framework and the joint problem is solved using the alternating direction method of multipliers. Results on document clustering show that our proposed approach improves over the state-of-the-art.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 37 references that are in the map. First posted on arXiv 27 Nov 2021.

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References · 12

  1. 2015Unsupervised Deep Embedding for Clustering Analysiscited by 763
  2. 2016Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clusteringcited by 237
  3. 2015Global Convergence of ADMM in Nonconvex Nonsmooth Optimizationcited by 98
  4. 2019Deep Spectral Clustering using Dual Autoencoder Networkcited by 71
  5. 2018Deep k-Means: Jointly clustering with k-Means and learning representationscited by 50
  6. 2015A deep matrix factorization method for learning attribute representationscited by 24
  7. 2015Efficient Blind Compressed Sensing Using Sparsifying Transforms with Convergence Guarantees and Application to MRIcited by 4
  8. 2017Face Sketch Matching via Coupled Deep Transform Learningcited by 4
  9. 2019Transformed Subspace Clusteringcited by 3
  10. 2019Simultaneous Detection of Multiple Appliances from Smart-meter Measurements via Multi-Label Consistent Deep Dictionary Learning and Deep Transform Learningcited by 2
  11. 2019Label Consistent Transform Learning for Hyperspectral Image Classificationcited by 1
  12. 2019Kernel Transform Learningcited by 1

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