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

Qingmin Liu; Kunru Huang

doi:10.5281/zenodo.20278468

May 18, 2026Open Access

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

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Key Points

The aim is to enhance dimensionality reduction techniques for psychometric system identification using tensor mapping.
Introduced a reconstructed model for dimensionality reduction involving tensor product spaces.
Analyzed local spacetime metrics to eliminate physically impossible states.
Developed a refined mathematical framework for effective dimensionality reduction.
Validated the feasibility of eliminating non-viable states in psychometric modeling.

Implication

Explores system identification through dimensionality reduction in psychometric assessments, suggesting a novel mathematical approach.

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.

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Cite This Study

Liu et al. (2026) studied this question.

synapsesocial.com/papers/6a0ea13abe05d6e3efb5fbf7 https://doi.org/10.5281/zenodo.20278468

View Full Paper

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

Key Points

Implication

Abstract

Discussion

Cite This Study

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

Key Points

Implication

Abstract

Discussion

Cite This Study

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