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

TL;DR

i-IF-Learn is an iterative unsupervised framework that jointly performs feature selection and clustering in high-dimensional data, effectively identifying influential features and improving clustering accuracy.

Contribution

It introduces an adaptive feature selection statistic that combines pseudo-labels with unsupervised signals, reducing error propagation in iterative clustering.

Findings

Outperforms classical and deep clustering methods on gene and single-cell datasets.

Selected influential features improve downstream deep learning models.

Demonstrates the importance of targeted feature selection in high-dimensional clustering.

Abstract

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.