Soil spectroscopy has experienced a tremendous increase in soil property characterisation, and can be used not only in the laboratory but also from the space (imaging spectroscopy). Partial least squares (PLS) regression is one of the most common approaches for the calibration of soil properties using soil spectra. Besides functioning as a calibration method, PLS can also be used as a dimension...

Partial Least Squares (PLS) dimension reduction is known to give good prediction accuracy in the context of classification with high-dimensional microarray data. In this paper, the classification procedure consisting of PLS dimension reduction and linear discriminant analysis on the new components is compared with some of the best state-of-the-art classification methods. Moreover, a boosting al...

BACKGROUND Primary lateral sclerosis (PLS) is a progressive upper motor neuron neurodegenerative condition. The diagnosis is made using clinical history, objective neurological assessment, and exclusion of other neurodegenerative disorders. OBJECTIVE To evaluate the role of fluorodeoxyglucose F18 positron emission tomography and 3-dimensional stereotactic surface projection in the diagnosis o...

Understanding the processes that determine the distribution of populations is a fundamental goal in ecology. In this study, I determined the relative contribution of space and the biotic and abiotic environment to the distribution of the palm-leaf skeletonizer Homaledra sabalella (PLS; Lepidoptera: Coleophoridae) among patchily distributed dwarf palmettos (Sabal minor; Arecaceae). Based on surv...

Partial least squares (PLS) has proven to be a important multivariate analytic tool for positron emission tomographic and, more recently, event-related potential (ERP) data. The application to ERP incorporates the ability to analyze space and time together, a feature that has obvious appeal for event-related functional magnetic resonance imaging (fMRI) data. This paper presents the extension of...

Segmentation is a critical issue for PLS path modeling. Group specific model estimates can significantly differ from other groups or the overall model estimates (Ringle, Wende, & Will, 2007). Sequential segmentation strategies on the manifest data level, such as K-means or tree clustering, usually fail to identify groups of data with distinctive inner path model estimates (Ringle, 2006). The au...