تقرير
Hyperbolic Delaunay Geometric Alignment
العنوان: | Hyperbolic Delaunay Geometric Alignment |
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المؤلفون: | Medbouhi, Aniss Aiman, Marchetti, Giovanni Luca, Polianskii, Vladislav, Kravberg, Alexander, Poklukar, Petra, Varava, Anastasia, Kragic, Danica |
سنة النشر: | 2024 |
المجموعة: | Computer Science |
مصطلحات موضوعية: | Computer Science - Machine Learning |
الوصف: | Hyperbolic machine learning is an emerging field aimed at representing data with a hierarchical structure. However, there is a lack of tools for evaluation and analysis of the resulting hyperbolic data representations. To this end, we propose Hyperbolic Delaunay Geometric Alignment (HyperDGA) -- a similarity score for comparing datasets in a hyperbolic space. The core idea is counting the edges of the hyperbolic Delaunay graph connecting datapoints across the given sets. We provide an empirical investigation on synthetic and real-life biological data and demonstrate that HyperDGA outperforms the hyperbolic version of classical distances between sets. Furthermore, we showcase the potential of HyperDGA for evaluating latent representations inferred by a Hyperbolic Variational Auto-Encoder. |
نوع الوثيقة: | Working Paper |
URL الوصول: | http://arxiv.org/abs/2404.08608 |
رقم الأكسشن: | edsarx.2404.08608 |
قاعدة البيانات: | arXiv |
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