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While previous methods like Convolutional Neural Networks (CNN) and sparse coding have shown promise, capturing continuous changes in stress-coping states remains challenging. To address this limitation, we focus on t-SNE for dimensionality reduction, which compresses high-dimensional facial thermal data while preserving both local and global structure. Our findings show that facial thermal images from the same stress-coping response cluster together in the reduced space, allowing continuous monitoring of facial skin temperature changes. Additionally, the behavior of the data in the reduced space revealed a time lag between hemodynamic parameter variations and facial skin temperature distribution changes. These insights contribute to developing models that can continuously track stress-coping state changes.<\/jats:p>","DOI":"10.1007\/s10015-025-01022-4","type":"journal-article","created":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T05:54:47Z","timestamp":1743832487000},"page":"424-431","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Discrimination of stress coping responses on dimensionality-reduced facial thermal image space"],"prefix":"10.1007","volume":"30","author":[{"given":"Masahito","family":"Takano","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shiori","family":"Oyama","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kent","family":"Nagumo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Akio","family":"Nozawa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,4,3]]},"reference":[{"key":"1022_CR1","unstructured":"Sawada Yo (1998) \u201cNew Physiological Psychology.\u201d Kitaoji Books : 172-195. 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