# Simple and Scalable Algorithms for Cluster-Aware Precision Medicine

arXiv: https://arxiv.org/abs/2211.16553 · HTML: https://mlsanity.com/paper/2211.16553/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 128 references that are in the map.
- First posted on arXiv 29 Nov 2022.

## Builds on (most cited first, 22 of 22)

- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [From Louvain to Leiden: guaranteeing well-connected communities](https://mlsanity.com/paper/1810.08473/) (2018)
- [Joint Unsupervised Learning of Deep Representations and Image Clusters](https://mlsanity.com/paper/1604.03628/) (2016)
- [On Deep Multi-View Representation Learning: Objectives and Optimization](https://mlsanity.com/paper/1602.01024/) (2016)
- [Scalable Sparse Subspace Clustering by Orthogonal Matching Pursuit](https://mlsanity.com/paper/1507.01238/) (2015)
- [Oracle Based Active Set Algorithm for Scalable Elastic Net Subspace Clustering](https://mlsanity.com/paper/1605.02633/) (2016)
- [Splitting Methods for Convex Clustering](https://mlsanity.com/paper/1304.0499/) (2013)
- [Convex Clustering: Model, Theoretical Guarantee and Efficient Algorithm](https://mlsanity.com/paper/1810.02677/) (2018)
- [A significance test for the lasso](https://mlsanity.com/paper/1301.7161/) (2013)
- [Statistical Properties of Convex Clustering](https://mlsanity.com/paper/1503.08340/) (2015)
- [Recovering Trees with Convex Clustering](https://mlsanity.com/paper/1806.11096/) (2018)
- [On perfect clustering of high dimension, low sample size data](https://mlsanity.com/paper/1612.09121/) (2016)
- [Dynamic Visualization and Fast Computation for Convex Clustering via Algorithmic Regularization](https://mlsanity.com/paper/1901.01477/) (2019)
- [Joint Optimization of an Autoencoder for Clustering and Embedding](https://mlsanity.com/paper/2012.03740/) (2020)
- [Integrative Generalized Convex Clustering Optimization and Feature Selection for Mixed Multi-View Data](https://mlsanity.com/paper/1912.05449/) (2019)
- [Recovery of a mixture of Gaussians by sum-of-norms clustering](https://mlsanity.com/paper/1902.07137/) (2019)
- [Hierarchically Clustered Representation Learning](https://mlsanity.com/paper/1901.09906/) (2019)
- [Splitting Methods for Convex Bi-Clustering and Co-Clustering](https://mlsanity.com/paper/1901.06075/) (2019)
- [A Centroid Auto-Fused Hierarchical Fuzzy c-Means Clustering](https://mlsanity.com/paper/2004.12756/) (2020)
- [Supervised Convex Clustering](https://mlsanity.com/paper/2005.12198/) (2020)
- [Adaptive testing for the graphical lasso](https://mlsanity.com/paper/1307.4765/) (2013)
- [Two-block vs. Multi-block ADMM: An empirical evaluation of convergence](https://mlsanity.com/paper/1907.04524/) (2019)

## Abstract (arXiv)

AI-enabled precision medicine promises a transformational improvement in healthcare outcomes by enabling data-driven personalized diagnosis, prognosis, and treatment. However, the well-known "curse of dimensionality" and the clustered structure of biomedical data together interact to present a joint challenge in the high dimensional, limited observation precision medicine regime. To overcome both issues simultaneously we propose a simple and scalable approach to joint clustering and embedding that combines standard embedding methods with a convex clustering penalty in a modular way. This novel, cluster-aware embedding approach overcomes the complexity and limitations of current joint embedding and clustering methods, which we show with straightforward implementations of hierarchically clustered principal component analysis (PCA), locally linear embedding (LLE), and canonical correlation analysis (CCA). Through both numerical experiments and real-world examples, we demonstrate that our approach outperforms traditional and contemporary clustering methods on highly underdetermined problems (e.g., with just tens of observations) as well as on large sample datasets. Importantly, our approach does not require the user to choose the desired number of clusters, but instead yields interpretable dendrograms of hierarchically clustered embeddings. Thus our approach improves significantly on existing methods for identifying patient subgroups in multiomics and neuroimaging data, enabling scalable and interpretable biomarkers for precision medicine.