The complex information systems such as enterprise resource planning (ERP) systems are essential for organizations to make them competitive. However, the success of ERP system projects is a difficult process as it involves different types of end user assessment. The main objective of the present study is to find the key determinants that open the door to employee satisfaction and adoption of E...

Partial Least Squares (PLS) has become an increasingly popular approach to testing research models with multiple proposed causality links. Moreover, recent interest in the specification of constructs in a formative manner has accentuated this tendency, given the purported ability of PLS to handle this methodological development. While a review of the literature reveals an extensive use of PLS i...

The prediction of the aero-engine performance parameters is very important for aero-engine condition monitoring and fault diagnosis. In this paper, the chaotic phase space of engine exhaust temperature EGT time series which come from actual air-borne ACARS data is reconstructed through selecting some suitable nearby points. The partial least square PLS based on the cubic spline function or the ...

Volatile compounds in fifty-eight Arabica roasted coffee samples from Brazil were analyzed by SPME-GC-FID and SPME-GC-MS, and the results were compared with those from sensory evaluation. The main purpose was to investigate the relationships between the volatile compounds from roasted coffees and certain sensory attributes, including body, flavor, cleanliness and overall quality. Calibration mo...

Principal components analysis is traditionally presented as an interpretive multivariate technique, where the loadings are chosen to maximally explain the variance in the variable. However, we will consider it here mainly as a statistical learning tool, by using the derived components in a least squares regression to predict unobserved response variables using the principal components. Principa...

Within the context of nonlinear system identification, different variants of LS-SVM are applied to the Silver Box dataset. Starting from the dual representation of the LS-SVM, and using Nyström techniques, it is possible to compute an approximation for the nonlinear mapping to be used in the primal space. In this way, primal space based techniques as Ordinary Least Squares (OLS), Ridge Regressi...

Glucose concentration measurement is the basis of noninvasive detection of blood glucose concentration. It is significant in scientific research. In this study, Near Infrared Spectroscopy (NIRS) and regression analysis methodology were combined to measure the glucose concentration. The spectrum of glucose solutions was obtained with the Fourier Transformed Infrared Spectrometer, and then the da...

This paper proposes learning-based methods for mapping a sparse representation of noisy speech to state likelihoods in an automatic speech recognition system. We represent speech as a sparse linear combination of exemplars extracted from training data. The weights of exemplars are mapped to speech state likelihoods using Ordinary Least Squares (OLS) and Partial Least Squares (PLS) regression. R...

Partial Least Squares Regression (PLSR) is a linear regression technique developed to deal with high-dimensional regressors and one or several response variables. In this paper we introduce robustified versions of the SIMPLS algorithm being the leading PLSR algorithm because of its speed and efficiency. Because SIMPLS is based on the empirical cross-covariance matrix between the response variab...