Essential Number of Principal Components and Nearly Training-Free Model for Spectral Analysis

• URL: http://arxiv.org/abs/2212.14623v1
• Date: Fri, 30 Dec 2022 10:19:10 GMT
• Title: Essential Number of Principal Components and Nearly Training-Free Model for Spectral Analysis
• Authors: Yifeng Bie and Shuai You and Xinrui Li and Xuekui Zhang and Tao Lu
• Abstract summary: We show that the number of functional or non-functional principal components required to retain the essential information is the same as the number of independent constituents in the mixture set. Due to the mutual in-dependency among different gas molecules, near one-to-one projection from the principal component to the mixture constituent can be established.
• Score: 5.374884291606767
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Through a study of multi-gas mixture datasets, we show that in multi-component spectral analysis, the number of functional or non-functional principal components required to retain the essential information is the same as the number of independent constituents in the mixture set. Due to the mutual in-dependency among different gas molecules, near one-to-one projection from the principal component to the mixture constituent can be established, leading to a significant simplification of spectral quantification. Further, with the knowledge of the molar extinction coefficients of each constituent, a complete principal component set can be extracted from the coefficients directly, and few to none training samples are required for the learning model. Compared to other approaches, the proposed methods provide fast and accurate spectral quantification solutions with a small memory size needed.

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