# Transformed K-means Clustering

arXiv: https://arxiv.org/abs/2111.13921 · HTML: https://mlsanity.com/paper/2111.13921/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 37 references that are in the map.
- First posted on arXiv 27 Nov 2021.

## Builds on (most cited first, 12 of 12)

- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering](https://mlsanity.com/paper/1610.04794/) (2016)
- [Global Convergence of ADMM in Nonconvex Nonsmooth Optimization](https://mlsanity.com/paper/1511.06324/) (2015)
- [Deep Spectral Clustering using Dual Autoencoder Network](https://mlsanity.com/paper/1904.13113/) (2019)
- [Deep k-Means: Jointly clustering with k-Means and learning representations](https://mlsanity.com/paper/1806.10069/) (2018)
- [A deep matrix factorization method for learning attribute representations](https://mlsanity.com/paper/1509.03248/) (2015)
- [Efficient Blind Compressed Sensing Using Sparsifying Transforms with Convergence Guarantees and Application to MRI](https://mlsanity.com/paper/1501.02923/) (2015)
- [Face Sketch Matching via Coupled Deep Transform Learning](https://mlsanity.com/paper/1710.02914/) (2017)
- [Transformed Subspace Clustering](https://mlsanity.com/paper/1912.04734/) (2019)
- [Simultaneous Detection of Multiple Appliances from Smart-meter Measurements via Multi-Label Consistent Deep Dictionary Learning and Deep Transform Learning](https://mlsanity.com/paper/1912.07568/) (2019)
- [Label Consistent Transform Learning for Hyperspectral Image Classification](https://mlsanity.com/paper/1912.11405/) (2019)
- [Kernel Transform Learning](https://mlsanity.com/paper/1912.12129/) (2019)

## 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.