Partial least squares regression and projection on latent structure regression (PLS Regression)

Abdi, Hervé

doi:10.1002/wics.51

Cited by 1,177 publications

(792 citation statements)

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“…Indeed, its goal is to predict the dependent variables (Y), selecting a limited number of new orthogonal factors (T) and a set of specific loadings (P) able to simultaneously both X and Y, in order to reduce the high dimensionality of the input dataset [8,22,33,36,[61][62][63][64]. Thus, X is calculated by applying the following equation:…”

Section: Partial Least Squares Regression (Plsr)mentioning

confidence: 99%

“…Typically, it increases until it reaches a certain number of LVs before declining again. For that reason, it is fundamental to select the optimal number of LVs for each model, which allow us to obtain the best quality [64][65][66]. In this study, the optimal number of LVs for each PLSR model per grassland trait was estimated based on the root mean square error (RMSE) for the leave-one-out cross-validation (LOOCV).…”

Section: Partial Least Squares Regression (Plsr)mentioning

confidence: 99%

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Estimating Plant Traits of Grasslands from UAV-Acquired Hyperspectral Images: A Comparison of Statistical Approaches

Capolupo

Kooistra

Berendonk³

et al. 2015

IJGI

123

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Grassland ecosystems cover around 40% of the entire Earth's surface. Therefore, it is necessary to guarantee good grassland management at field scale in order to improve its conservation and to achieve optimal growth. This study identified the most appropriate statistical strategy, between partial least squares regression (PLSR) and narrow vegetation indices, for estimating the structural and biochemical grassland traits from UAV-acquired hyperspectral images. Moreover, the influence of fertilizers on plant traits for grasslands was analyzed. Hyperspectral data were collected from an experimental field at the farm Haus Riswick, near Kleve in Germany, for two different flight campaigns in May and October. The collected image blocks were geometrically and radiometrically corrected for surface reflectance. Spectral signatures extracted for the plots were adopted to derive grassland traits by computing PLSR and the following narrow vegetation indices: the MERIS Terrestrial Chlorophyll Index (MTCI), the ratio of the Modified Chlorophyll Absorption in Reflectance and Optimized Soil-Adjusted Vegetation Index (MCARI/OSAVI) modified by Wu, the Rededge Chlorophyll Index (CIred-edge), and the Normalized Difference Red Edge (NDRE). PLSR showed promising results for estimating grassland structural traits and gave less satisfying outcomes for the selected chemical traits (crude ash, crude fiber, crude protein, Na, K, metabolic energy). Established relations are not influenced by the type and the amount of fertilization, while they are affected by the grassland health status. PLSR is found to be the best strategy, among the approaches analyzed in this paper, for exploring structural and biochemical features of grasslands. Using UAV-based hyperspectral sensing allows for the highly detailed assessment of grassland experimental plots.

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Section: Partial Least Squares Regression (Plsr)mentioning

confidence: 99%

Section: Partial Least Squares Regression (Plsr)mentioning

confidence: 99%

Estimating Plant Traits of Grasslands from UAV-Acquired Hyperspectral Images: A Comparison of Statistical Approaches

Capolupo

Kooistra

Berendonk³

et al. 2015

IJGI

123

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“…(Insert Table 2 about here) modeling the former as predictor variables and the latter inputted as dependent variables, using partial least squares (PLS) regression, which allows for multiple independent variables (the 10 variables comprising the GREENER framework in this case) to predict multiple dependent variables (the 18 EBPIs) (Abdi, 2010). PLS has become widely used and recognized in general customer satisfaction research as well as identifying success factors in the marketing literature (Henseler, Ringle, & Sinkovics, 2009).…”

Section: ) Comparing and Contrastingmentioning

confidence: 99%

“…The utility of PLS over traditional regression approaches lies in the way dependent variables are modeled via determination of their common structure with the predictors, from which parameter estimates are based (Abdi, 2010;Höskuldsson, 1988;Wold, Sjöström, & Eriksson, 2001). An alternative analytical technique would have been to use canonical correlation analysis (CCA), which maximizes the correlation between two sets of variables by minimizing the covariance between them (Fornell & Larcker, 1987).…”

Section: ) Comparing and Contrastingmentioning

confidence: 99%

Scoring environmental credentials: a review of UK conference and meetings venues using the GREENER VENUE framework

Whitfield

Dioko

Webber

2013

Journal of Sustainable Tourism

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Abstract:This paper presents an investigation into frameworks used to conceptualise, monitor and compare corporate social responsibility (CSR). Presenting a new comparative framework that organises and communicates information detailing business performance on ten environmental policy initiatives (expressed by the acronym 'GREENER') using a CSR scale (expressed by the acronym 'VENUE'). This GREENER VENUE framework fills reduces the gap in the CSR literature by accentuating discretionary practices, by exhibiting conceptual and psychometric properties enabling its application to broad and diverse contexts. Grounded in theory, the framework is practical, simple to implement, easily understandable and highly relatable. Applying the GREENER VENUE framework to data collected via a self-administered Internet questionnaire of the UK conference and meeting venues reveals the majority of such venues are classified as 'Eager'. This paper presents an investigation into frameworks used to conceptualise, monitor and compare corporate social responsibility (CSR). Presenting a new comparative framework that organises and communicates information detailing business performance on ten environmental policy initiatives (expressed by the acronym 'GREENER') using a CSR scale (expressed by the acronym 'VENUE'). This GREENER VENUE framework fills reduces the gap in the CSR literature by accentuating discretionary practices, by exhibiting conceptual and psychometric properties enabling its application to broad and diverse contexts.Grounded in theory, the framework is practical, simple to implement, easily understandable and highly relatable. Applying the GREENER VENUE framework to data collected via a self-administered Internet questionnaire of the UK conference and meeting venues reveals the majority of such venues are classified as 'Eager'.

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“…PLS regression method [30], [31] is based on the linear transformation of a large number of descriptors to a new space based on a small number of orthogonal projection vectors. In other words, the projection vectors are mutually independent linear combinations of the original descriptors.…”

Section: A Video Characterizationmentioning

confidence: 99%

Video-Based Face Spoofing Detection through Visual Rhythm Analysis

Pinto

Pedrini

Schwartz

et al. 2012

2012 25th SIBGRAPI Conference on Graphics, Patterns and Images

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Fig. 1. Given a training set consisting of videos of valid accesses, video-based spoofs, and a video for testing, first we extract a noise signature of every video (training and testing) and calculate the Fourier Spectrum on logarithmic scale for each video frame. Thereafter, we create visual rhythms for each video and train a machine learning classifier using either the pixel intensities directly as features or a summarized version of the visual rhythms using gray level co-occurrence matrices. With a trained classifier, we are able to test a visual rhythm for a given video under investigation and point out whether it is a valid access or a video-based spoof.Abstract-Recent advances on biometrics, information forensics, and security have improved the accuracy of biometric systems, mainly those based on facial information. However, an ever-growing challenge is the vulnerability of such systems to impostor attacks, in which users without access privileges try to authenticate themselves as valid users. In this work, we present a solution to video-based face spoofing to biometric systems. Such type of attack is characterized by presenting a video of a real user to the biometric system. To the best of our knowledge, this is the first attempt of dealing with video-based face spoofing based in the analysis of global information that is invariant to video content. Our approach takes advantage of noise signatures generated by the recaptured video to distinguish between fake and valid access. To capture the noise and obtain a compact representation, we use the Fourier spectrum followed by the computation of the visual rhythm and extraction of the graylevel co-occurrence matrices, used as feature descriptors. Results show the effectiveness of the proposed approach to distinguish between valid and fake users for video-based spoofing with nearperfect classification results.

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Partial least squares regression and projection on latent structure regression (PLS Regression)

Cited by 1,177 publications

References 34 publications

Estimating Plant Traits of Grasslands from UAV-Acquired Hyperspectral Images: A Comparison of Statistical Approaches

Estimating Plant Traits of Grasslands from UAV-Acquired Hyperspectral Images: A Comparison of Statistical Approaches

Scoring environmental credentials: a review of UK conference and meetings venues using the GREENER VENUE framework

Video-Based Face Spoofing Detection through Visual Rhythm Analysis

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