Results 31 to 40 of about 115,342 (279)
Improving Dimensionality Reduction Projections for Data Visualization
Applied Sciences, 2023 In data science and visualization, dimensionality reduction techniques have been extensively employed for exploring large datasets. These techniques involve the transformation of high-dimensional data into reduced versions, typically in 2D, with the aim ...
Bardia Rafieian +2 more
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Manifold Learning in MR spectroscopy using nonlinear dimensionality reduction and unsupervised clustering [PDF]
, 2014 Purpose To investigate whether nonlinear dimensionality reduction improves unsupervised classification of 1H MRS brain tumor data compared with a linear method. Methods In vivo single-voxel 1H magnetic resonance spectroscopy (55 patients) and 1H magnetic
Barrick, TR +3 more
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A HJB-POD approach for the control of nonlinear PDEs on a tree structure [PDF]
, 2019 The Dynamic Programming approach allows to compute a feedback control for nonlinear problems, but suffers from the curse of dimensionality. The computation of the control relies on the resolution of a nonlinear PDE, the Hamilton-Jacobi-Bellman equation ...
Alla, Alessandro, Saluzzi, Luca
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Linear Hamilton Jacobi Bellman Equations in High Dimensions [PDF]
, 2014 The Hamilton Jacobi Bellman Equation (HJB) provides the globally optimal solution to large classes of control problems. Unfortunately, this generality comes at a price, the calculation of such solutions is typically intractible for systems with more than
Burdick, Joel W. +2 more
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IEEE Access, 2022 The human hand needs a large number of sensors to measure kinematics owing to its large number of degrees of freedom. Existing devices like data gloves and optical trackers are associated with calibration, line of sight, and accuracy problems.
Prajwal Shenoy +2 more
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Optimized kernel minimum noise fraction transformation for hyperspectral image classification [PDF]
, 2017 This paper presents an optimized kernel minimum noise fraction transformation (OKMNF) for feature extraction of hyperspectral imagery. The proposed approach is based on the kernel minimum noise fraction (KMNF) transformation, which is a nonlinear ...
Gao, Lianru +4 more
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Distortion-Free Nonlinear Dimensionality Reduction [PDF]
, 2008 Nonlinear Dimensionality Reduction is an important issue in many machine learning areas where essentially low-dimensional data is nonlinearly embedded in some high-dimensional space. In this paper, we show that the existing Laplacian Eigenmaps method suffers from the distortion problem, and propose a new distortion-free dimensionality reduction method ...
Yangqing Jia +2 more
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Dimensionality Reduction Mappings [PDF]
, 2011 A wealth of powerful dimensionality reduction methods has been established which can be used for data visualization and preprocessing. These are accompanied by formal evaluation schemes, which allow a quantitative evaluation along general principles and ...
Biehl, Michael +3 more
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Probabilistic Nonlinear Dimensionality Reduction
, 2022 High-dimensional datasets are present across scientific disciplines. In the analysis of such datasets, dimensionality reduction methods which provide clear interpretations of their model parameters are required. Principal components analysis (PCA) has long been a preferred method for linear dimensionality reduction, but is not recommended for data ...
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Semisupervised Kernel Marginal Fisher Analysis for Face Recognition
The Scientific World Journal, 2013 Dimensionality reduction is a key problem in face recognition due to the high-dimensionality of face image. To effectively cope with this problem, a novel dimensionality reduction algorithm called semisupervised kernel marginal Fisher analysis (SKMFA ...
Ziqiang Wang +3 more
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