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New LORE framework learns dimensionality and structure from ordinal data

By PulseAugur Editorial · [1 sources] ·

Researchers have developed LORE (Low Rank Ordinal Embedding), a novel framework designed to learn the intrinsic dimensionality and ordinal structure from subjective perceptual data. Unlike previous methods that require pre-setting the embedding dimension, LORE utilizes a nonconvex Schatten-p quasi norm for automatic joint recovery of both the embedding and its dimensionality. The framework employs an iteratively reweighted algorithm for optimization and has demonstrated effectiveness in experiments with synthetic, simulated, and real-world ordinal judgments, leading to compact and interpretable low-dimensional embeddings. AI

IMPACT Introduces a new method for modeling subjective percepts and discovering low-dimensional structure from ordinal data.

RANK_REASON The cluster contains a research paper detailing a new machine learning framework. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New LORE framework learns dimensionality and structure from ordinal data

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The cluster contains a research paper detailing a new machine learning framework. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Vivek Anand, Alec Helbling, Mark A. Davenport, Gordon J. Berman, Sankaraleengam Alagapan, Christopher John Rozell ·

    LORE: Jointly Learning the Intrinsic Dimensionality and Relative Similarity Structure From Ordinal Data

    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…

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