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

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.