English(EN) LORE: Jointly Learning the Intrinsic Dimensionality and Relative Similarity Structure From Ordinal Data

新的LORE框架从序数数据中学习维度和结构

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员开发了LORE（低秩序数嵌入），一个旨在从主观感知数据中学习内在维度和序数结构的新框架。与先前需要预设嵌入维度的方法不同，LORE利用非凸Schatten-p拟范数自动联合恢复嵌入及其维度。该框架采用迭代重加权算法进行优化，并在合成、模拟和真实世界序数判断的实验中证明了其有效性，从而得到紧凑且可解释的低维嵌入。 AI

影响引入了一种对主观感知进行建模以及从序数数据中发现低维结构的新方法。

排序理由该集群包含一篇详细介绍新机器学习框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Vivek Anand, Alec Helbling, Mark A. Davenport, Gordon J. Berman, Sankaraleengam Alagapan, Christopher John Rozell · 2026-09-15 04:00

LORE：从序数数据中联合学习内在维度和相对相似性结构

arXiv:2602.04192v3 Announce Type: replace Abstract: Learning the intrinsic dimensionality of subjective perceptual spaces such as taste, smell, or aesthetics from ordinal data is a challenging problem. We introduce LORE (Low Rank Ordinal Embedding), a scalable framework that join…

本文如何被排名

报道来源 [1]

LORE：从序数数据中联合学习内在维度和相对相似性结构

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