• Publisher-DOI
• Publisher
• Research data
• Identical work

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.

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