Visible and near-infrared hyperspectral imaging covering spectral range of 380-1030 nm as a rapid and non-destructive method was applied to estimate the soluble protein content of oilseed rape leaves. Average spectrum (500-900 nm) of the region of interest (ROI) of each sample was extracted, and four samples out of 128 samples were defined as outliers by Monte Carlo-partial least squares (MCPLS...

In order to improve fitting and forecasting precision and solve the problem that some data with less sensitivity lead to low simulation precision of partial leastsquares regression (PLS for short) model, the new method of simulating freezing depth is presented according to ground temperature of different depths, air temperatures, surface temperatures and the like. Firstly, the PLS model, which ...

In this paper I review covariance-based Partial Least Squares (PLS) methods, focusing on common features of their respective algorithms and optimization criteria. I then show how these algorithms can be adjusted for use as optimal scaling tools. Three new PLS-type algorithms are proposed for the analysis of one, two or several blocks of variables: the NonMetric NIPALS, the Non-Metric PLS Regres...

Partial least squares (PLS) methods have recently been used for many pattern recognition problems in computer vision. Here, PLS is primarily used as a supervised dimensionality reduction tool to obtain effective feature combinations for better learning. However, application of PLS to large datasets is hindered by its higher computational cost. We propose an approach to accelerate the classical ...

Univariate partial least squares (PLS) is a method of modeling relationships between a Y variable and other explanatory vanables. It may be used with any number of explanatory variables, even far more than the number of observations. A simple interpretation is given that shows the method to be a straightforward and reasonable way of forming prediction equations. Its relationship to multivariate...

4 We build connections between envelopes, a recently proposed context for efficient estima5 tion in multivariate statistics, and multivariate partial least squares (PLS) regression. In partic6 ular, we establish an envelope as the nucleus of both univariate and multivariate PLS, which 7 opens the door to pursuing the same goals as PLS but using different envelope estimators. It 8 is argued that...