Results 61 to 70 of about 26,804 (294)
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
Advanced Engineering Materials, EarlyView.This article presents the design, modeling, and characterization of air‐pressure–actuated programmable vibroacoustic metamaterials (PVAMM). The study focuses on leveraging air pressure to dynamically tune resonance frequencies for effective noise attenuation.
William Kaal +2 more
wiley +1 more source
Advanced Engineering Materials, EarlyView.Mg–Zn composites with a thickness of 0.21 mm were fabricated using roll bonding of a kirigami‐patterned Mg alloy inlay within a Zn matrix. Thermal activation following this process led to the formation of tailored intermetallic structures, which provided the composite with enhanced flexural strength.
Yaroslav Frolov +4 more
wiley +1 more source
Nonlinear dimensionality reduction of gene expression data [PDF]
, 2006 Using microarray measurements techniques, it is possible to measure the activity of genes simultaneously across the whole genome. Since genes influence each others activity levels through complex regulatory networks, such gene expression measurements are
Nilsson, Jens
core
Learning a kernel matrix for nonlinear dimensionality reduction [PDF]
Twenty-first international conference on Machine learning - ICML '04, 2004 We investigate how to learn a kernel matrix for high dimensional data that lies on or near a low dimensional manifold. Noting that the kernel matrix implicitly maps the data into a nonlinear feature space, we show how to discover a mapping that "unfolds" the underlying manifold from which the data was sampled.
Kilian Q. Weinberger +2 more
openaire +1 more source
Advanced Engineering Materials, EarlyView.A simplified thermoplastic pultrusion model is developed to predict thermal fields in glass fiber/polyethylene terephthalate (GF/PET) composites with reduced computational cost. By combining effective material homogenization, validation against literature data, and Gaussian‐process‐based optimization, the study reveals how heating limits, pulling speed,
Elder Soares +3 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
Fostering Innovation: Streamlining Magnetocaloric Materials Research by Digitalization
Advanced Engineering Materials, EarlyView.Magnetocaloric cooling (MCE) is an environmentally friendly refrigeration method with great potential. Optimizing MCE materials involves the preparation and screening of large quantities of samples, which in turn generates a large amount of data. A digitalization approach is presented that uses ontologies, knowledge graphs, and digital workflows to ...
Simon Bekemeier +17 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
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