Dimensionality Reduction of Local Spacetime Metrics via Discrete Tensor Mapping for Psychometric System Identification

Update in Rev 2: Significantly revised Section 4 to introduce a completely reconstructed physical and mathematical model for dimensionality reduction, specifically detailing the tensor product space and eliminating physically impossible states.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-05-18
DOI
https://doi.org/10.5281/zenodo.20278468
Primary Topic
Tensor decomposition and applications
Type
preprint
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preprint

Dimensionality Reduction of Local Spacetime Metrics via Discrete Tensor Mapping for Psychometric System Identification

Qingmin Liu, Kunru Huang
Zenodo (CERN European Organization for Nuclear Research)
Tensor decomposition and applications
preprint

Dimensionality Reduction of Local Spacetime Metrics via Discrete Tensor Mapping for Psychometric System Identification

Qingmin Liu, Kunru Huang
preprint en

Abstract

Update in Rev 2: Significantly revised Section 4 to introduce a completely reconstructed physical and mathematical model for dimensionality reduction, specifically detailing the tensor product space and eliminating physically impossible states.

Zenodo (CERN European Organization for Nuclear Research)
Insight (China) (CN)
Tensor decomposition and applications
DOIOpen Access PDFOpenAlex
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Dimensionality Reduction of Local Spacetime Metrics via Discrete Tensor Mapping for Psychometric System Identification — Qingmin Liu, Kunru Huang · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS