Results 211 to 220 of about 72,540 (253)
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Domain-Invariant Partial-Least-Squares Regression
Analytical Chemistry, 2018Multivariate calibration models often fail to extrapolate beyond the calibration samples because of changes associated with the instrumental response, environmental condition, or sample matrix. Most of the current methods used to adapt a source calibration model to a target domain exclusively apply to calibration transfer between similar analytical ...
Ramin Nikzad-Langerodi +3 more
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Sparse Kernel Partial Least Squares Regression
2003Partial 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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Spectral Partial Least Squares Regression
IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS, 2010Linear 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, 1986Abstract 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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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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A twist to partial least squares regression
Journal of Chemometrics, 2005AbstractA 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), 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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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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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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