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Variable selection using axis-aligned random projections for partial least-squares regression
In high-dimensional data modeling, variable selection plays a crucial role in improving predictive accuracy and enhancing model interpretability...
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Locally sparse and robust partial least squares in scalar-on-function regression
We present a novel approach for estimating a scalar-on-function regression model, leveraging a functional partial least squares methodology. Our...
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Quality-related Fault Detection Based on Approximate Kernel Partial Least Squares Method
The kernel partial least squares (KPLS) method has been widely used in quality-related fault detection since it can acquire the features of the...
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Sparse functional partial least squares regression with a locally sparse slope function
The partial least squares approach has been particularly successful in spectrometric prediction in chemometrics. By treating the spectral data as...
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Enhancing age-related postural sway classification using partial least squares-discriminant analysis and hybrid feature set
Feature sets in a machine learning algorithm can have an impact on the robustness, interpretability, and characterization of the data. To detect...
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Componentwise Least Squares Support Vector Machines
This chapter describes componentwise Least Squares Support Vector Machines (LS-SVMs) for the estimation of additive models consisting of a sum of...
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Logarithmic Least Squares Method
In this chapter, we present logarithmic least squares method (LLSM) for priority for incomplete fuzzy reciprocal preference relations. LLSM method is...
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Classification of multivariate functional data on different domains with Partial Least Squares approaches
Classification (supervised-learning) of multivariate functional data is considered when the elements of the random functional vector of interest are...
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Fast Global Image Smoothing via Quasi Weighted Least Squares
Image smoothing is a long-studied research area with tremendous approaches proposed. However, how to perform high-quality image smoothing with less...
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An improved multi-task least squares twin support vector machine
In recent years, multi-task learning (MTL) has become a popular field in machine learning and has a key role in various domains. Sharing knowledge...
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An Improved Regression Partial Least Squares Method for Quality-Related Process Monitoring of Industrial Control Systems
Partial least squares (PLS) is a widely used and effective method in the field of fault detection. However, due to the fact that the standard PLS...
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Fuzzy Least Squares Support Vector Machine with Fuzzy Hyperplane
This study uses fuzzy set theory for least squares support vector machines (LS-SVM) and proposes a novel formulation that is called a fuzzy...
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ECT for flow imaging: total least squares for image reconstruction algorithm
For the problem that noise has a great impact on the measurement data during the electrical capacitance tomography data acquisition process, a...
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Penalized Least Squares Classifier: Classification by Regression Via Iterative Cost-Sensitive Learning
Least squares estimate that can directly obtain the analytical solution to minimize the mean square error (MSE) is one of the most effective...
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Image classification based on weighted nonconvex low-rank and discriminant least squares regression
Classifiers based on least squares regression (LSR) are effective in multi-classification tasks. However, there are two main problems that greatly...
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A real unconstrained equivalent problem of the quaternion equality constrained weighted least squares problem
In this paper, we focus on the quaternion equality constrained weighted least squares problem. First, according to the properties of the...
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Spherical interpolation of scattered data using least squares thin-plate spline and inverse multiquadric functions
We construct some smooth functions defined over a sphere that interpolate large sets of scattered data, using some modified Shepard methods, the...
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Exploring oversampling in RBF least-squares collocation method of lines for surface diffusion
This paper investigates the numerical behavior of the radial basis functions least-squares collocation (RBF-LSC) method of lines (MoL) for solving...
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A new numerical algorithm based on least squares method for solving stochastic Itô-Volterra integral equations
In conjunction with least squares method and generalized hat functions, we propose a new algorithm for stochastic Itô-Volterra integral equations....
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On the robustness of exponential base terms and the Padé denominator in some least squares sense
The exponential analysis of
2 n uniformly collected samples from ann -term exponential sum is equivalent to the reconstruction of a rational function...