Political Space

Pearson, Elizabeth H.

doi:10.1002/9781118300664.ch28

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…PCA was used to reduce the dimensions of the rating score data. This analysis was first proposed by Pearson and later developed by Hotelling . In this study, PCA was applied to simplify visual assessment responses to determine the emotional response components that affected a person's perception of coloured metallic paints.…”

Section: Methodsmentioning

confidence: 99%

Assessing appearance and emotional attributes of coloured metallic paints

Huang

Chen

Luo

et al. 2017

Coloration Technology

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The objective of this study was to investigate the agreement between multiangle appearance data obtained from multiangle measurements and the rating scores given by observers in terms of sparkle and coarseness. The metallic emotional equation for coloured metallic paints was developed on the basis of two experimental results. Experiment 1 involved measuring metallic paint appearance, and a multiangle spectrophotometer was used for measuring the appearance parameters of metallic coating samples under multiple measuring angles. In Experiment 2, observers were asked to visually assess coloured metallic samples under directional and diffuse light conditions. The observers assessed the test samples, illuminated from the top, from three observation angles. Principal component analysis was performed to analyse the observers' emotional responses. Finally, the metallic emotional models consisting of the expensiveness model and fashion model were constructed.

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Section: Methodsmentioning

confidence: 99%

Assessing appearance and emotional attributes of coloured metallic paints

Huang

Chen

Luo

et al. 2017

Coloration Technology

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“…One of the well‐known methods for dimensionality reduction is the principal component analysis (PCA). [ 204 ] The idea behind PCA is to approximate particular data with linear manifolds of lower dimensions. PCA can be alternatively interpreted as finding subspaces of lower dimension in the orthogonal projection on which the data variation is maximum.…”

Section: Mathematical Formulation Of Applicationsmentioning

confidence: 99%

Analog Photonics Computing for Information Processing, Inference, and Optimization

Nikita

Berloff

2023

Adv Quantum Tech

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This review presents an overview of the current state‐of‐the‐art in photonics computing, which leverages photons, photons coupled with matter, and optics‐related technologies for effective and efficient computational purposes. It covers the history and development of photonics computing and modern analogue computing platforms and architectures, focusing on optimization tasks and neural network implementations. The authors examine special‐purpose optimizers, mathematical descriptions of photonics optimizers, and their various interconnections. Disparate applications are discussed, including direct encoding, logistics, finance, phase retrieval, machine learning, neural networks, probabilistic graphical models, and image processing, among many others. The main directions of technological advancement and associated challenges in photonics computing are explored, along with an assessment of its efficiency. Finally, the paper discusses prospects and the field of optical quantum computing, providing insights into the potential applications of this technology.

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Political Space

Cited by 2 publications

References 15 publications

Assessing appearance and emotional attributes of coloured metallic paints

Assessing appearance and emotional attributes of coloured metallic paints

Analog Photonics Computing for Information Processing, Inference, and Optimization

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