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

• URL: http://arxiv.org/abs/2602.04192v1
• Date: Wed, 04 Feb 2026 04:12:56 GMT
• Title: LORE: Jointly Learning the Intrinsic Dimensionality and Relative Similarity Structure From Ordinal Data
• Authors: Vivek Anand, Alec Helbling, Mark Davenport, Gordon Berman, Sankar Alagapan, Christopher Rozell,
• Abstract summary: We introduce LORE (Low Rank Embedding), a framework that learns both intrinsic dimensionality and ordinal structure jointly.<n>Experiments on synthetic datasets, simulated perceptual spaces, and real world crowdsourced ordinal embeddings show that LORE learns compact, interpretable and highly accurate low dimensional embeddings.
• Score: 5.829457190465839
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 jointly learns both the intrinsic dimensionality and an ordinal embedding from noisy triplet comparisons of the form, "Is A more similar to B than C?". Unlike existing methods that require the embedding dimension to be set apriori, LORE regularizes the solution using the nonconvex Schatten-$p$ quasi norm, enabling automatic joint recovery of both the ordinal embedding and its dimensionality. We optimize this joint objective via an iteratively reweighted algorithm and establish convergence guarantees. Extensive experiments on synthetic datasets, simulated perceptual spaces, and real world crowdsourced ordinal judgements show that LORE learns compact, interpretable and highly accurate low dimensional embeddings that recover the latent geometry of subjective percepts. By simultaneously inferring both the intrinsic dimensionality and ordinal embeddings, LORE enables more interpretable and data efficient perceptual modeling in psychophysics and opens new directions for scalable discovery of low dimensional structure from ordinal data in machine learning.

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