Results 281 to 290 of about 397,898 (309)

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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Microwave characterization using partial least square regression

2016 IEEE Conference on Electromagnetic Field Computation (CEFC), 2016
Inverse problems for determination of dielectric materials properties (complex permittivity) are usually solved by iterative methods using numerically based forward model. These methods are computationally expensive. In this paper, we propose a fast inversion model based on partial least square regression (PLSR).
Sadou, Hakim, Hacib, Tarik, Le Bihan, Yann, Acikgoz, Hulusi, Meyer, Olivier   +4 more
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Partial least squares regression for graph mining

Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, 2008
Attributed graphs are increasingly more common in many application domains such as chemistry, biology and text processing. A central issue in graph mining is how to collect informative subgraph patterns for a given learning task. We propose an iterative mining method based on partial least squares regression (PLS).
Saigo H., Kramer N., Tsuda K.
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PoLiSh — smoothed partial least-squares regression

Analytica Chimica Acta, 2001
Partial least-squares (PLS) regression is a very widely used technique in spectroscopy for calibration/prediction purposes. One of the most important steps in the application of the PLS regression is the determination of the correct number of dimensions to use in order to avoid over-fitting, and therefore to obtain a robust predictive model.
Douglas N. Rutledge, António Barros, Ivonne Delgadillo   +2 more
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Orthogonal Nonlinear Partial Least-Squares Regression

Industrial & Engineering Chemistry Research, 2003
A multivariate statistical regression technique is proposed to address underlying nonlinear correlations among the predictor variables, as well as between the predictor variables and the response variable. The method is based on a neural network architecture that preserves the orthogonality properties of the principal component analysis (PCA) approach.
Fuat Doymaz, Ahmet Palazoglu, Jose A. Romagnoli   +2 more
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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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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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Extreme partial least-squares regression

2021
A 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, Enjolras, Geoffroy, Girard, Stéphane   +2 more
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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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Robust methods for partial least squares regression

Journal of Chemometrics, 2003
AbstractPartial 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, this being the leading PLSR algorithm because of its speed and efficiency.
M. Hubert, K. Vanden Branden
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