Results 51 to 60 of about 26,804 (294)
Parametric nonlinear dimensionality reduction using kernel t-SNE [PDF]
, 2015 Gisbrecht A, Schulz A, Hammer B. Parametric nonlinear dimensionality reduction using kernel t-SNE. Neurocomputing.
Schulz, Alexander ; https://orcid.org/ +2 more
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A tied-weight autoencoder for the linear dimensionality reduction of sample data
Scientific ReportsDimensionality reduction is a method used in machine learning and data science to reduce the dimensions in a dataset. While linear methods are generally less effective at dimensionality reduction than nonlinear methods, they can provide a linear ...
Sunhee Kim +3 more
doaj +1 more source
Advanced Engineering Materials, EarlyView.Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt +8 more
wiley +1 more source
Forward Stepwise Deep Autoencoder-Based Monotone Nonlinear Dimensionality Reduction Methods [PDF]
, 2020 Dimensionality reduction is an unsupervised learning task aimed at creating a low-dimensional summary and/or extracting the most salient features of a dataset.
Youyi Fong (519951), Jun Xu (45543)
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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
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Improving reduced-order models through nonlinear decoding of projection-dependent outputs
Patterns, 2023 Summary: A fundamental hindrance to building data-driven reduced-order models (ROMs) is the poor topological quality of a low-dimensional data projection.
Kamila Zdybał +2 more
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Advanced Engineering Materials, EarlyView.This study explores the lightweight potential of laser additive‐manufactured NiTi triply periodic minimal surface sheet lattices. It systematically investigates the effects of relative density and unit cell size on surface quality, deformation recovery, compression behavior, and energy absorption.
Haoming Mo +3 more
wiley +1 more source
Advanced Engineering Materials, EarlyView.A numerical–experimental framework is developed for characterizing multi‐matrix fiber‐reinforced polymers (MM‐FRPs) combining epoxy and polyurethane matrices. Harmonic bending tests are integrated with finite element model updating (FEMU) to simultaneously identify elastic and viscoelastic material parameters.
Rodrigo M. Dartora +4 more
wiley +1 more source
Nonlinear dimensionality reduction using approximate nearest neighbors [PDF]
, 2007 Nonlinear dimensionality reduction methods often rely on the nearest-neighbors graph to extract low-dimensional embeddings that reliably capture the underlying structure of high-dimensional data.
Lydia E. Kavraki, Erion Plaku
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Behavior of Linear and Nonlinear Dimensionality Reduction for Collective Variable Identi cation of Small Molecule Solution-Phase Reactions [PDF]
, 2021 Identifying collective variables for chemical reactions is essential to reduce the 3$N$ dimensional energy landscape into lower dimensional basins and barriers of interest.
Ernesto, Martinez-Baez +6 more
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