Density‐Clustering Decoding of Plasma Micro‐Region Dynamic Molecular Fingerprint Spectra

ABSTRACT

Intelligent dimensionality reduction of high‐dimensional spectral data requires data compression while preserving physically meaningful optical‐response structures. Here, we propose a density‐sensitive clustering‐driven multimodal dimensionality reduction evaluation framework (DSC‐MD) to quantitatively assess physical structure fidelity during optical feature‐space construction. By integrating local neighborhood relationships, unsupervised density clustering, orthogonalized structural metrics, and cross‐scale robustness validation, DSC‐MD provides a classifier‐independent pre‐evaluation of feature extraction strategies under a fixed representation capacity. We apply DSC‐MD to plasma micro‐region dynamic molecular fingerprint spectra acquired over multiple delay times, where wavelength, emission intensity, and delay jointly form a spatiotemporal data cube governed by femtosecond laser‐matter interactions. The framework evaluates whether low‐dimensional representations retain neighborhood organization, density distributions, local topologies, and time‐correlated spectral evolution. For plastic spectra, Principal Component Analysis (PCA) better preserves relatively continuous temporal evolutionary trajectories, whereas ceramic spectra exhibit stronger nonlinear neighborhood structures better matched by Kernel PCA (KPCA). These results show that DSC‐MD offers a physics‐guided, data‐adaptive evaluation paradigm for high‐throughput ultrafast optical signals, providing a pre‐evaluation basis for dynamic molecular‐fragmentation identification, future quantum tracking of molecular cleavage, and retention assessment of material‐specific dynamic optical fingerprints in complex condensed‐phase environments.