i-IF-Learn: Iterative Feature Selection and Unsupervised Learning for High-Dimensional Complex Data
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 frame...
| Authors | Chen Ma, Wanjie Wang, Shuhao Fan |
|---|---|
| Primary category | cs.LG |
| Published | 25 March 2026 |
| Last updated at source | 5 July 2026 |
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