Results 251 to 260 of about 91,920 (292)

The objective function of partial least squares regression

Journal of Chemometrics, 1998
A simple objective function in terms of undeflated X is derived for the latent variables of multivariate PLS regression. The objective function fits into the basic framework put forward by Burnham et al. (J. Chemometrics, 10, 31–45 (1996)). We show that PLS and SIMPLS differ in the constraint put on the length of the X-weight vector.
ter Braak, C.J.F., de Jong, S.
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Partial Least‐Squares Regression

2013
This chapter presents the most widely applied and, probably, satisfactory multivariate regression method used nowadays: partial least squares (PLS). Graphical explanations of many concepts are given to complement the more formal mathematical background. Several approaches to solving current problems are suggested.
José Manuel Andrade‐Garda, Alatzne Carlosena‐Zubieta, Ricard Boqué‐Martí, Joan Ferré‐Baldrich   +3 more
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Spectral Partial Least Squares Regression

IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS, 2010
Linear Graph Embedding (LGE) is the linearization of graph embedding, and has been applied in many domains successfully. However, the high computational cost restricts these algorithms to be applied to large scale high dimensional data sets. One major limitation of such algorithms is that the generalized eigenvalue problem is computationally expensive ...
Jiangfeng Chen, Baozong Yuan
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Partial least-squares regression: a tutorial

Analytica Chimica Acta, 1986
Abstract A tutorial on the partial least-squares (PLS) regression method is provided. Weak points in some other regression methods are outlined and PLS is developed as a remedy for those weaknesses. An algorithm for a predictive PLS and some practical hints for its use are given.
Paul Geladi, Bruce R. Kowalski
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Sparse Kernel Partial Least Squares Regression

2003
Partial Least Squares Regression (PLS) and its kernel version (KPLS) have become competitive regression approaches. KPLS performs as well as or better than support vector regression (SVR) for moderately-sized problems with the advantages of simple implementation, less training cost, and easier tuning of parameters.
Michinari Momma, Kristin P. Bennett
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Stacked partial least squares regression for image classification

2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), 2015
In recent years, the researches based on Convolutional Neural Network (CNN) have been doing in computer vision after the success in ILSVRC 2012. Hierarchical feature extraction is one of the reasons why CNN gives the state-of-the-art performance. On the other hand, Partial Least Squares (PLS) Regression which has been widely used in chemo-metrics is ...
Ryoma Hasegawa, Kazuhiro Hotta
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A twist to partial least squares regression

Journal of Chemometrics, 2005
AbstractA modification of the PLS1 algorithm is presented. Stepwise optimization over a set of candidate loading weights obtained by taking powers of the y–X correlations and X standard deviations generalizes the classical PLS1 based on y–X covariances and hence adds flexibility to the modelling.
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Craniofacial landmarks extraction by Partial Least Squares Regression

2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512), 2004
In this paper, a novel method based on Partial Least Square Regression (PLSR) is introduced to extract the relation between selected point coordinates on X-ray images and the expected location of a set of landmarks formally known as craniofacial landmarks. In the proposed method, four points are located using image detection techniques. The four points
Idris El-Feghi, Yasser M. Alginahi, Maher A. Sid-Ahmed, Majid Ahmadi   +3 more
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Study of partial least squares and ridge regression methods

Communications in Statistics - Simulation and Computation, 2016
ABSTRACTThis article considers both Partial Least Squares (PLS) and Ridge Regression (RR) methods to combat multicollinearity problem. A simulation study has been conducted to compare their performances with respect to Ordinary Least Squares (OLS).
Luis Firinguetti, B. M. Golam Kibria, Rodrigo Araya   +2 more
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Voice Conversion Using Partial Least Squares Regression

IEEE Transactions on Audio, Speech, and Language Processing, 2010
Voice conversion can be formulated as finding a mapping function which transforms the features of the source speaker to those of the target speaker. Gaussian mixture model (GMM)-based conversion is commonly used, but it is subject to overfitting. In this paper, we propose to use partial least squares (PLS)-based transforms in voice conversion.
Elina Helander, Tuomas Virtanen, Jani Nurminen, Moncef Gabbouj   +3 more
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