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• W1529564978 abstract "Mathematical treatments and modelling of large data structures have always created problems. From the infancy of computers to the late 1980s, the limiting factor when modelling large data structures was often the size of the computer memory. Due to the strong evolution in the Held of computer technology, this problem is steadily decreasing. Consequently, when hardware restrictions are becoming less significant, one allows for the development of new, interesting but also calculation-intensive techniques. Typical examples within the area of drug design are techniques like 3D QSAR and molecular library characterization and modelling. However, improved hardware puts the focus on other limiting factors such as speed and efficiency of the mathematical operations performed when processing data. Algorithms and programs must be refined and optimized to meet the demands of today. The desired ‘interactiveness’ in data processing and molecular modelling serves as a good example of the needs of a modern drug design chemist. A group of data-analytical tools which steadily increase their applicability are the latent variable based ones, such as Principal Components analysis (PCA) [1,2]; Principal Components Regression (PCR) [3]; and Partial Least-squares Regression (PLS) [4-18]. Especially in the disciplines of natural science, their impact has been large during the past few decades, even if statistical methods based on diagonalization of covariance matrices have been used earlier. The usefulness and advantages of projection methods have been discussed by several authors, and for their introduction and applicability we refer to the vast literature [1-22]. However, these methods are frequently studied and their algorithms have been subjects for refinement and optimization. In this chapter, we will focus on the further developments of the PLS algorithm, using the classical algorithm as a reference for comparison. During the past years, several authors have published modified PLS algorithms with the main aim of increasing the computational speed. Often the code is optimized for a certain type of computational job or a special shape of data matrix. One common step which ties all new developments together is the calculation of some useful variance/covariance and association matrices. Our aim is to point out some commonalities and differences between the individual PLS algorithms in a simple and transparent way. No deep-penetrating computational evaluation was carried out. Instead, the paper will provide a detailed reference list of original articles." @default.
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• W1529564978 date "2005-12-16" @default.
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• W1529564978 title "Alternative Partial Least-Squares (PLS) Algorithms" @default.
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• W1529564978 doi "https://doi.org/10.1007/0-306-46858-1_7" @default.
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