mlsanityData to 13 Oct 2026

Perceptual adjustment queries and an inverted measurement paradigm for low-rank metric learning

arXiv 2309.04626 ↗First posted 8 Sep 2023stat.ML cs.AI cs.ITcited by 3

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Abstract (arXiv)

We introduce a new type of query mechanism for collecting human feedback, called the perceptual adjustment query ( PAQ). Being both informative and cognitively lightweight, the PAQ adopts an inverted measurement scheme, and combines advantages from both cardinal and ordinal queries. We showcase the PAQ in the metric learning problem, where we collect PAQ measurements to learn an unknown Mahalanobis distance. This gives rise to a high-dimensional, low-rank matrix estimation problem to which standard matrix estimators cannot be applied. Consequently, we develop a two-stage estimator for metric learning from PAQs, and provide sample complexity guarantees for this estimator. We present numerical simulations demonstrating the performance of the estimator and its notable properties.

Key facts. Cited by 3 papers in the map (data through 13 Oct 2026). Builds on 21 of its 54 references that are in the map. First posted on arXiv 8 Sep 2023.

Builds on

21 of 54 references.

References · 21

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9 more
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Cited by year (2026 to 13 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Metric Learning in an RKHScited by 0
  2. 2025Ranking Items from Discrete Ratings: The Cost of Unknown User Thresholdscited by 0
  3. 2026Toward individual-level calibration in affect recognition with perceptual adjustment queriescited by 0