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Stacked partial least squares regression for image classification
2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), 2015In 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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On the structure of partial least squares regression
Communications in Statistics - Simulation and Computation, 1988We prove that the two algorithms given in the literature for partial least squares regression are equivalent, and use this equivalence to give an explicit formula for the resulting prediction equation. This in turn is used to investigate the regression method from several points of view. Its relation to principal component regression is clearified, and
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Craniofacial landmarks extraction by Partial Least Squares Regression
2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512), 2004In 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 +3 more
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Extreme partial least-squares regression
2021A new approach, called Extreme-PLS, is proposed for dimension reduction in regression and adapted to distribution tails. The goal is to find linear combinations of predictors that best explain the extreme values of the response variable by maximizing the associated covariance.
Bousebata, Meryem +2 more
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Study of partial least squares and ridge regression methods
Communications in Statistics - Simulation and Computation, 2016ABSTRACTThis 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 +2 more
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Voice Conversion Using Partial Least Squares Regression
IEEE Transactions on Audio, Speech, and Language Processing, 2010Voice 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 +3 more
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Partial Least Squares Regression for Beta Regression Models
2021International ...
Bertrand, Frédéric +1 more
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An overview on the shrinkage properties of partial least squares regression
Computational Statistics, 2007Shrinkage factors (SF) for partial least squares (PLS) estimates of linear regression coefficients are evaluated. They are compared to the shrinkage factors of principal components regression and ridge regression techniques. It is shown that SF for PLS can be greater then 1.
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Partial least trimmed squares regression
Chemometrics and Intelligent Laboratory Systems, 2022Zhonghao Xie, Xi'an Feng, Xiaojing Chen
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Partial least squares methods for functional regression models
2018An important class of prediction problems in modern biomedical studies is to use medical images as well as genetic and clinical biomarkers at an earlier time point to predict important clinical outcomes, including continuous, discrete, survival and longitudinal outcomes, at a later time point.
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