# Subject clustering by IF-PCA and several recent methods

arXiv: https://arxiv.org/abs/2306.05363 · HTML: https://mlsanity.com/paper/2306.05363/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 54 references that are in the map.
- First posted on arXiv 8 Jun 2023.

## Builds on (most cited first, 14 of 14)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Generating Diverse High-Fidelity Images with VQ-VAE-2](https://mlsanity.com/paper/1906.00446/) (2019)
- [A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues](https://mlsanity.com/paper/1605.06069/) (2016)
- [Theoretical Foundations of t-SNE for Visualizing High-Dimensional Clustered Data](https://mlsanity.com/paper/2105.07536/) (2021)
- [A Selective Overview of Deep Learning](https://mlsanity.com/paper/1904.05526/) (2019)
- [Tensor Clustering with Planted Structures: Statistical Optimality and Computational Limits](https://mlsanity.com/paper/2005.10743/) (2020)
- [Estimation of the number of spiked eigenvalues in a covariance matrix by bulk eigenvalue matching analysis](https://mlsanity.com/paper/2006.00436/) (2020)
- [Influential Feature PCA for high dimensional clustering](https://mlsanity.com/paper/1407.5241/) (2014)
- [A smart local moving algorithm for large-scale modularity-based community detection](https://mlsanity.com/paper/1308.6604/) (2013)
- [Detection and Feature Selection in Sparse Mixture Models](https://mlsanity.com/paper/1405.1478/) (2014)
- [Phase Transitions for High Dimensional Clustering and Related Problems](https://mlsanity.com/paper/1502.06952/) (2015)
- [A Spectral Method for Assessing and Combining Multiple Data Visualizations](https://mlsanity.com/paper/2210.13711/) (2022)
- [Comparing large covariance matrices under weak conditions on the dependence structure and its application to gene clustering](https://mlsanity.com/paper/1505.04493/) (2015)
- [Distance Assisted Recursive Testing](https://mlsanity.com/paper/2103.11085/) (2021)

## Built on by (most cited first, 1 of 1)

- [i-IF-Learn: Iterative Feature Selection and Unsupervised Learning for High-Dimensional Complex Data](https://mlsanity.com/paper/2603.24025/) (2026)

## Abstract (arXiv)

Subject clustering (i.e., the use of measured features to cluster subjects, such as patients or cells, into multiple groups) is a problem of great interest. In recent years, many approaches were proposed, among which unsupervised deep learning (UDL) has received a great deal of attention. Two interesting questions are (a) how to combine the strengths of UDL and other approaches, and (b) how these approaches compare to one other. We combine Variational Auto-Encoder (VAE), a popular UDL approach, with the recent idea of Influential Feature PCA (IF-PCA), and propose IF-VAE as a new method for subject clustering. We study IF-VAE and compare it with several other methods (including IF-PCA, VAE, Seurat, and SC3) on 10 gene microarray data sets and 8 single-cell RNA-seq data sets. We find that IF-VAE significantly improves over VAE, but still underperforms IF-PCA. We also find that IF-PCA is quite competitive, which slightly outperforms Seurat and SC3 over the 8 single-cell data sets. IF-PCA is conceptually simple and permits delicate analysis. We demonstrate that IF-PCA is capable of achieving the phase transition in a Rare/Weak model. Comparatively, Seurat and SC3 are more complex and theoretically difficult to analyze (for these reasons, their optimality remains unclear).