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Fast hyperboloid decision tree algorithms
March 6, 2024, 5:43 a.m. | Philippe Chlenski, Ethan Turok, Antonio Moretti, Itsik Pe'er
cs.LG updates on arXiv.org arxiv.org
Abstract: Hyperbolic geometry is gaining traction in machine learning for its effectiveness at capturing hierarchical structures in real-world data. Hyperbolic spaces, where neighborhoods grow exponentially, offer substantial advantages and consistently deliver state-of-the-art results across diverse applications. However, hyperbolic classifiers often grapple with computational challenges. Methods reliant on Riemannian optimization frequently exhibit sluggishness, stemming from the increased computational demands of operations on Riemannian manifolds. In response to these challenges, we present hyperDT, a novel extension of decision …
abstract advantages algorithms applications art arxiv challenges classifiers computational cs.lg data decision diverse diverse applications geometry hierarchical machine machine learning optimization results spaces state tree type world
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