Klir, Stefan ; Fathia, Reda ; Babilon, Sebastian ; Benkner, Simon ; Khanh, Tran Quoc (2022)
Unsupervised Clustering Pipeline to Obtain Diversified Light Spectra for Subject Studies and Correlation Analyses.
In: Applied Sciences, 2022, 11 (19)
doi: 10.26083/tuprints-00021256
Artikel, Zweitveröffentlichung, Verlagsversion
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Kurzbeschreibung (Abstract)
Featured Application: Selection of most diverse light spectra from a larger set of possible candidates to be used in subject studies or for machine learning to find correlations between photometric and other parameters such as psychological, physiological, or preference-based outcome measures.
Abstract: Current subject studies and data-driven approaches in lighting research often use manually selected light spectra, which usually exhibit a large bias due to the applied selection criteria. This paper, therefore, presents a novel approach to minimize this bias by using a data-driven framework for selecting the most diverse candidates from a given larger set of possible light spectra. The spectral information per wavelength is first reduced by applying a convolutional autoencoder. The relevant features are then selected based on Laplacian Scores and transformed to a two-dimensional embedded space for subsequent clustering. The low dimensional embedding, from which the required diversity follows, is done with respect to the locality of the features. In a second step, photometric parameters are considered and a second clustering is performed. As a result of this algorithmic pipeline, the most diverse selection of light spectra complying with a given set of relevant photometric parameters can be extracted and used for further experiments or applications.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Klir, Stefan ; Fathia, Reda ; Babilon, Sebastian ; Benkner, Simon ; Khanh, Tran Quoc |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | Unsupervised Clustering Pipeline to Obtain Diversified Light Spectra for Subject Studies and Correlation Analyses |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Publikationsdatum der Erstveröffentlichung: | 2022 |
| Verlag: | MDPI |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Applied Sciences |
| Jahrgang/Volume einer Zeitschrift: | 11 |
| (Heft-)Nummer: | 19 |
| Kollation: | 18 Seiten |
| DOI: | 10.26083/tuprints-00021256 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/21256 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichung aus gefördertem Golden Open Access |
| Kurzbeschreibung (Abstract): | Featured Application: Selection of most diverse light spectra from a larger set of possible candidates to be used in subject studies or for machine learning to find correlations between photometric and other parameters such as psychological, physiological, or preference-based outcome measures. Abstract: Current subject studies and data-driven approaches in lighting research often use manually selected light spectra, which usually exhibit a large bias due to the applied selection criteria. This paper, therefore, presents a novel approach to minimize this bias by using a data-driven framework for selecting the most diverse candidates from a given larger set of possible light spectra. The spectral information per wavelength is first reduced by applying a convolutional autoencoder. The relevant features are then selected based on Laplacian Scores and transformed to a two-dimensional embedded space for subsequent clustering. The low dimensional embedding, from which the required diversity follows, is done with respect to the locality of the features. In a second step, photometric parameters are considered and a second clustering is performed. As a result of this algorithmic pipeline, the most diverse selection of light spectra complying with a given set of relevant photometric parameters can be extracted and used for further experiments or applications. |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-212563 |
| Zusätzliche Informationen: | This article belongs to the Special Issue Machine Learning and Signal Processing for IOT Applications Keywords: light clustering; diversified light spectra; spectral embedding; light selection; spectral feature selection |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 600 Technik, Medizin, angewandte Wissenschaften > 600 Technik |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Adaptive Lichttechnische Systeme und Visuelle Verarbeitung |
| Hinterlegungsdatum: | 06 Mai 2022 12:04 |
| Letzte Änderung: | 09 Mai 2022 09:14 |
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| Suche nach Titel in: | TUfind oder in Google |
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- Unsupervised Clustering Pipeline to Obtain Diversified Light Spectra for Subject Studies and Correlation Analyses. (deposited 06 Mai 2022 12:04) [Gegenwärtig angezeigt]
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