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This arXiv preprint proposes NEST, an unsupervised method that trains a single embedding so that its nested prefix subspaces capture coarse-to-fine cluster structure, without requiring a predefined hierarchy. The reported results are preliminary and come from synthetic hierarchies and vision, text, and single-cell benchmarks, so the practical value depends on how well the approach holds up against stronger baselines. For ML readers, the idea of treating clustering granularity as a property of the representation, borrowed from Matryoshka-style training, is the main thing to evaluate.
NEST: Nested Embedding Representation for Multiscale Clustering
arXiv:2610.30520v1 Announce Type: new Abstract: Real-world data often exhibits cluster structure at multiple resolutions, yet existing methods typically learn a single partition or construct an explicit hierarchy. Inspired by Matryoshka Representation Learning, we introduce NEST, an unsupervised framework that encodes coarse-to-fine structure within a single embedding. NEST jointly optimizes clustering and separation across nested prefix subspaces, enabling progressively finer distinctions as embedding dimensionality increases, with rank and within-cluster regularization that keep the embedding from collapsing. Empirically, distances in the learned embedding follow the hierarchy distance more closely than those of the input, although the hierarchy is never observed during training. On synthetic hierarchies, NEST recovers multiscale structure given only the number of clusters per level. Experiments on vision, text, and single-cell data further demonstrate multiscale organization across real-world representations. By making clustering granularity an intrinsic property of the representation, NEST provides a unified approach to multiscale structure discovery.
Abstract:Real-world data often exhibits cluster structure at multiple resolutions, yet existing methods typically learn a single partition or construct an explicit hierarchy. Inspired by Matryoshka Representation Learning, we introduce NEST, an unsupervised framework that encodes coarse-to-fine structure within a single embedding. NEST jointly optimizes clustering and separation across nested prefix subspaces, enabling progressively finer distinctions as embedding dimensionality increases, with rank and within-cluster regularization that keep the embedding from collapsing. Empirically, distances in the learned embedding follow the hierarchy distance more closely than those of the input, although the hierarchy is never observed during training. On synthetic hierarchies, NEST recovers multiscale structure given only the number of clusters per level. Experiments on vision, text, and single-cell data further demonstrate multiscale organization across real-world representations. By making clustering granularity an intrinsic property of the representation, NEST provides a unified approach to multiscale structure discovery.
Machine Learning (cs.LG)
arXiv:2610.30520 [cs.LG]
(or arXiv:2610.30520v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.30520 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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