Results 71 to 80 of about 478,588 (342)
Semi-Supervised Discriminant Analysis Using Robust Path-Based Similarity [PDF]
, 2008 Linear Discriminant Analysis (LDA), which works by maximizing the within-class similarity and minimizing the between-class similarity simultaneously, is a popular dimensionality reduction technique in pattern recognition and machine learning.
Dit-yan Yeung, Yu Zhang
core +2 more sources
Journal of Applied Clinical Medical Physics, EarlyView.Abstract This article investigates the performance of a commercial BeO optically stimulated luminescent (OSL) dosimetry system (myOSLchip, RadPro GmbH International, Remscheid, Germany) through the application of the commissioning framework for luminescent dosimeters as described in the American Association of Physicists in Medicine Task Group 191 ...
Joseph P. Kowalski +4 more
wiley +1 more source
Exact Model Reduction for Damped-Forced Nonlinear Beams: An Infinite-Dimensional Analysis [PDF]
, 2017 We use invariant manifold results on Banach spaces to conclude the existence of spectral submanifolds (SSMs) in a class of nonlinear, externally forced beam oscillations. SSMs are the smoothest nonlinear extensions of spectral subspaces of the linearized beam equation.
arxiv +1 more source
Lie symmetries and reductions of multi-dimensional boundary value problems of the Stefan type [PDF]
J. Phys. A: Math. Theor. 44 (2011) 485202, 2012 A new definition of Lie invariance for nonlinear multi-dimensional boundary value problems (BVPs) is proposed by the generalization of known definitions to much wider classes of BVPs. The class of (1+3)-dimensional nonlinear BVPs of the Stefan type, modeling the process of melting and evaporation of metals, is studied in detail.
arxiv +1 more source
nipalsMCIA: Flexible Multi-Block Dimensionality Reduction in R via Nonlinear Iterative Partial Least Squares [PDF]
BioinformaticsMax Mattessich +6 more
openalex +2 more sources
Array, 2021 High-dimensional data with many features are usually challenging to represent with standard visualization techniques. Usually, one has to resort to dimensionality reduction techniques such as PCA, MDS or t-SNE to represent such data.
Adrien Bibal +3 more
doaj
A New Approach to Improve the Topological Stability in Non-Linear Dimensionality Reduction
IEEE Access, 2020 Dimensionality reduction in the machine learning field mitigates the undesired properties of high-dimensional spaces to facilitate classification, compression, and visualization of high-dimensional data.
Mohammed Elhenawy +3 more
doaj +1 more source
nPCA: a linear dimensionality reduction method using a multilayer perceptron
Frontiers in GeneticsBackground: Linear dimensionality reduction techniques are widely used in many applications. The goal of dimensionality reduction is to eliminate the noise of data and extract the main features of data.
Juzeng Li, Yi Wang, Yi Wang
doaj +1 more source
IET Signal Processing, 2022 Isomap is a well‐known nonlinear dimensionality reduction method that highly suffers from computational complexity. Its computational complexity mainly arises from two stages; a) embedding a full graph on the data in the ambient space, and b) a complete ...
Eysan Mehrbani, Mohammad Hossein Kahaei
doaj +1 more source
Combination of inverse spectral transform method and method of characteristics: deformed Pohlmeyer equation [PDF]
, 2007 We apply a version of the dressing method to a system of four dimensional nonlinear Partial Differential Equations (PDEs), which contains both Pohlmeyer equation (i.e. nonlinear PDE integrable by the Inverse Spectral Transform Method) and nonlinear matrix PDE integrable by the method of characteristics as particular reductions.
arxiv +1 more source