When Features Become Instances: Inverted Contrastive Learning for Unsupervised Feature Selection

arXiv:2609.00782 · cs.AI, cs.CL · Submitted 2026-09-01 · Read on arXiv

cs.AI, cs.CL

Submitted: 2026-09-01

Updated: 2026-09-01

Code: https://github.com/neilghos/ICLFS

Project page: https://jundongl.github.io/scikit-feature/datasets.html

Terminology

• Feature Selection — 60× in this paper · explained in 4 episodes
• Clustering — 24× in this paper · explained in 2 episodes
• Embeddings — 21× in this paper · explained in 2 episodes
• Accuracy — 18× in this paper · explained in 1 episode
• Representation Learning — 18× in this paper · explained in 2 episodes
• Consistency — 14× in this paper · explained in 3 episodes
• Benchmark — 13× in this paper · explained in 2 episodes
• Contrastive Learning — 13× in this paper · explained in 5 episodes
• Laplacian-Gated Ranking Correction — 11× in this paper
• Laplacian Score — 10× in this paper
• Redundancy — 10× in this paper · explained in 2 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Self-Supervised Learning — 6× in this paper · explained in 8 episodes
• Sparsity — 5× in this paper · explained in 4 episodes
• Core — 4× in this paper · explained in 2 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Multi-Cluster Feature Selection — 2× in this paper
• Nonnegative Discriminative Feature Selection — 2× in this paper
• Adam — 1× in this paper · explained in 2 episodes
• Adaptive Selection — 1× in this paper · explained in 1 episode
• Aleatoric Uncertainty — 1× in this paper · explained in 5 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Interpretability — 1× in this paper · explained in 15 episodes
• Inverted Contrastive Learning with Feature Screening — 1× in this paper
• Robustness — 1× in this paper · explained in 5 episodes
• Spectral clustering — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Representation Learning with Contrastive Predictive Coding
• Rethinking Self-Supervised Learning: Small is Beautiful

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