Results 71 to 80 of about 115,342 (279)
Error Metrics for Learning Reliable Manifolds from Streaming Data
, 2017 Spectral dimensionality reduction is frequently used to identify low-dimensional structure in high-dimensional data. However, learning manifolds, especially from the streaming data, is computationally and memory expensive.
Chandola, Varun +4 more
core +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
Locality constrained dictionary learning for non‐linear dimensionality reduction and classification
IET Computer Vision, 2017 In view of the incremental dimensionality reduction problem of existing non‐linear dimensionality reduction methods, a novel algorithm, based on locality constrained dictionary learning (LCDL), is proposed in this study.
Lina Liu, Shiwei Ma, Ling Rui, Jian Lu
doaj +1 more source
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
Comprehensive review of dimensionality reduction algorithms: challenges, limitations, and innovative solutions [PDF]
PeerJ Computer ScienceDimensionality reduction (DR) simplifies complex data from genomics, imaging, sensors, and language into interpretable forms that support visualization, clustering, and modeling.
Aasim Ayaz Wani
doaj +2 more sources
Advanced Engineering Materials, EarlyView.This article demonstrates the successful qualification of a copper–tungsten composite for laser powder bed fusion. The resulting components exhibited high density, high thermal conductivity, and reduced thermal expansion. Heat sinks with complex geometries were successfully manufactured, clearly showcasing the material's potential for additive ...
Simon Rauh +6 more
wiley +1 more source
EFFECTIVENESS OF DIMENSIONALITY REDUCTION METHODS ON DATA WITH NON-LINEAR RELATIONSHIPS
BarekengThe phenomenon of big data presents distinct challenges in the analysis process, especially when the data contains a very large number of variables.
Lukmanul Hakim +4 more
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
Out-of-Sample Extension for Dimensionality Reduction of Noisy Time Series
, 2017 This paper proposes an out-of-sample extension framework for a global manifold learning algorithm (Isomap) that uses temporal information in out-of-sample points in order to make the embedding more robust to noise and artifacts. Given a set of noise-free
Dadkhahi, Hamid +2 more
core +1 more source
Advanced Engineering Materials, EarlyView.Stabilization of L‐PBF Ni50.7Ti49.3 under low‐cycle loading was investigated. Recoverable strain after cycling was dependent on the amount of applied load. Recovery ratio was 53.4% and 35.1% at intermediate and high load, respectively. The maximum total strain reached 10.3% at a high load of 1200 MPa.
Ondřej Červinek +5 more
wiley +1 more source