# i-IF-Learn: Iterative Feature Selection and Unsupervised Learning for High-Dimensional Complex Data

arXiv: https://arxiv.org/abs/2603.24025 · HTML: https://mlsanity.com/paper/2603.24025/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 56 references that are in the map.
- First posted on arXiv 25 Mar 2026.

## Builds on (most cited first, 11 of 11)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Deep Clustering for Unsupervised Learning of Visual Features](https://mlsanity.com/paper/1807.05520/) (2018)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering](https://mlsanity.com/paper/1611.05148/) (2016)
- [Interpretable Deep Clustering for Tabular Data](https://mlsanity.com/paper/2306.04785/) (2023)
- [Influential Feature PCA for high dimensional clustering](https://mlsanity.com/paper/1407.5241/) (2014)
- [Phase Transitions for High Dimensional Clustering and Related Problems](https://mlsanity.com/paper/1502.06952/) (2015)
- [Algorithmic Stability and Generalization of an Unsupervised Feature Selection Algorithm](https://mlsanity.com/paper/2010.09416/) (2020)
- [NeurCAM: Interpretable Neural Clustering via Additive Models](https://mlsanity.com/paper/2408.13361/) (2024)
- [Uniform error bound for PCA matrix denoising](https://mlsanity.com/paper/2306.12690/) (2023)
- [Subject clustering by IF-PCA and several recent methods](https://mlsanity.com/paper/2306.05363/) (2023)

## Abstract (arXiv)

Unsupervised learning of high-dimensional data is challenging due to irrelevant or noisy features obscuring underlying structures. It's common that only a few features, called the influential features, meaningfully define the clusters. Recovering these influential features is helpful in data interpretation and clustering. We propose i-IF-Learn, an iterative unsupervised framework that jointly performs feature selection and clustering. Our core innovation is an adaptive feature selection statistic that effectively combines pseudo-label supervision with unsupervised signals, dynamically adjusting based on intermediate label reliability to mitigate error propagation common in iterative frameworks. Leveraging low-dimensional embeddings (PCA or Laplacian eigenmaps) followed by k-means, i-IF-Learn simultaneously outputs influential feature subset and clustering labels. Numerical experiments on gene microarray and single-cell RNA-seq datasets show that i-IF-Learn significantly surpasses classical and deep clustering baselines. Furthermore, using our selected influential features as preprocessing substantially enhances downstream deep models such as DeepCluster, UMAP, and VAE, highlighting the importance and effectiveness of targeted feature selection. Code is available at: [https://github.com/mc25800852/iiflearn].