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18 papers in the last four weeks, up 260% on the four weeks before. 0.2% of all new papers.

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i-IF-Learn: Iterative Feature Selection and Unsupervised Learning for High-Dimensional Complex Data
Mar 25, 2026Chen Ma, Wanjie Wang, Shuhao FanFeature Selection Unsupervised
SUSTech · NUS
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Shanghai Artificial Intelligence Laboratory · Fudan University · University of Science and Technology of China +5
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Department of Electrical and Computer Engineering, Yale University, New Haven, CT.
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Department of Computer Science & Engineering, Texas A&M University, TX, USA · Kempner Institute, Harvard University, MA, USA
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Apr 9, 2025Or Feldman, Krishna Sri Ipsit Mantri, Carola-Bibiane Schönlieb +2Temporal Graph Neural Networks Zero-Shot Link Prediction
Ben-Gurion University of the Negev, Israel · Purdue University, USA · University of Cambridge, UK
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Apr 3, 2025Weibin Liao, Xin Gao, Tianyu Jia +6Text-To-Sql Natural Language
School of Computer Science, Peking University · 2Key Laboratory of High Confidence Software Technologies, Ministry of Education · School of Computer Sciences, Beijing University of Posts and Telecommunications +5
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Tencent

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

Chen Ma, Wanjie Wang, Shuhao Fan

SUSTech · NUS

Feature Selection Unsupervised High-Dimensional Transcriptomics Learn

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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/i_if_learn].

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