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Semi-supervised partial least squares
International Journal of Wavelets, Multiresolution and Information Processing, 2020Traditional supervised dimensionality reduction methods can establish a better model often under the premise of a large number of samples. However, in real-world applications where labeled data are scarce, traditional methods tend to perform poorly because of overfitting.
Xi Jin 0003 +4 more
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2000
Partial Least Squares (PLS), also known as Projection to Latent Structures, is a dimensionality reduction technique for maximizing the covariance between the predictor (independent) matrix X and the predicted (dependent) matrix Y for each component of the reduced space [61, 235].
Leo H. Chiang +2 more
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Partial Least Squares (PLS), also known as Projection to Latent Structures, is a dimensionality reduction technique for maximizing the covariance between the predictor (independent) matrix X and the predicted (dependent) matrix Y for each component of the reduced space [61, 235].
Leo H. Chiang +2 more
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Shrinkage Structure of Partial Least Squares
Scandinavian Journal of Statistics, 2000Partial least squares regression (PLS) is one method to estimate parameters in a linear model when predictor variables are nearly collinear. One way to characterize PLS is in terms of the scaling (shrinkage or expansion) along each eigenvector of the predictor correlation matrix. This characterization is useful in providing a link between PLS and other
Lingjærde, Ole C., Christophersen, Nils
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Kernel Partial Least-Squares Regression
The 2006 IEEE International Joint Conference on Neural Network Proceedings, 2006A couple of regularized least squares regression models in a feature space are extended by the kernel partial least squares (KPLS) regression model in this paper. PLS is a method based on the projection of input (explanatory) variables to the latent variables (components), and has been developed and established as one of the multivariate statistical ...
Bai Yifeng, Xiao Jian, Yu Long
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Partially Generalized Least Squares and Two-Stage Least Squares Estimators
Journal of Econometrics, 1983Abstract A class of partially generalized least squares estimators and a class of partially generalized two-stage least squares estimators in regression models with heteroscedastic errors are proposed. By using these estimators a researcher can attain higher efficiency than that attained by the least squares or the two-stage least squares estimators ...
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Partial Least Squares Image Clustering
2015 28th SIBGRAPI Conference on Graphics, Patterns and Images, 2015Clustering techniques have been widely used in areas that handle massive amounts of data, such as statistics, information retrieval, data mining and image analysis. This work presents a novel image clustering method called Partial Least Square Image Clustering (PLSIC), which employs a one against-all Partial Least Squares classifier to find image ...
Ricardo Barbosa Kloss +4 more
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Vehicle Detection Using Partial Least Squares
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011Detecting vehicles in aerial images has a wide range of applications, from urban planning to visual surveillance. We describe a vehicle detector that improves upon previous approaches by incorporating a very large and rich set of image descriptors. A new feature set called Color Probability Maps is used to capture the color statistics of vehicles and ...
Aniruddha Kembhavi +2 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, 2008Attributed 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).
Hiroto Saigo +2 more
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Partial Least Squares Path Modeling
2017Structural equation modeling (SEM) is a family of statistical techniques that has become very popular in marketing. Its ability to model latent variables, to take various forms of measurement error into account, and to test entire theories makes it useful for a plethora of research questions.
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A Probabilistic Derivation of the Partial Least-Squares Algorithm
Journal of Chemical Information and Computer Sciences, 2001Traditionally the partial least-squares (PLS) algorithm, commonly used in chemistry for ill-conditioned multivariate linear regression, has been derived (motivated) and presented in terms of data matrices. In this work the PLS algorithm is derived probabilistically in terms of stochastic variables where sample estimates calculated using data matrices ...
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