Unsupervised Clustering Pipeline to Obtain Diversified Light Spectra for Subject Studies and Correlation Analyses

• Published in: Applied sciences Vol. 11; no. 19; p. 9062
• Main Authors: Klir, Stefan, Fathia, Reda, Babilon, Sebastian, Benkner, Simon, Khanh, Tran Quoc
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2021
• Subjects: Algorithms; Bias; diversified light spectra; light clustering; Light emitting diodes; light selection; Lighting; Principal components analysis; spectral embedding; spectral feature selection
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app11199062

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.