AI RESEARCH
i-IF-Learn: Iterative Feature Selection and Unsupervised Learning for High-Dimensional Complex Data
arXiv CS.LG
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ArXi:2603.24025v1 Announce Type: new 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.