arXiv Statistics ML
Deep Clustering Evaluation: How to Validate Internal Clustering Validation Measures
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The paper introduces a selective inference framework tailored for deep clustering that uses a fixed pretrained encoder to map high‑dimensional data into a latent space before clustering. It addresses the complex selection bias arising from the nonlinear transformation and offers a computationally tractable method to perform valid statistical tests on cluster differences. Experiments on synthetic data show controlled Type I error and higher power compared to conservative baselines, while genomic case studies demonstrate the ability to uncover significant cluster differences while properly accounting for selection bias.
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Ensemble of Unsupervised Deep Learning for Clustering Imbalanced Tabular Data
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TopoMap: A Feature-based Discriminator of the Topographical Regions in the Test Input Space
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