Dimensionality reduction to solve resource allocation problem in 5G UDN using genetic algorithm
Sign Up to like & getrecommendations! Published in 2021 at "Soft Computing"
DOI: 10.1007/s00500-020-05473-8
Abstract: 5G ultra-dense network (UDN) systems consist of massive deployment of small cells. This technology allows increasing spectral efficiency and solving the spectrum scarcity problem. However, as small cell count increases, the probability of severe interference… read more here.
Keywords: resource allocation; cell; dimensionality reduction; allocation problem ... See more keywords
Effect of Legendre–Fenchel denoising and SVD-based dimensionality reduction algorithm on hyperspectral image classification
Sign Up to like & getrecommendations! Published in 2017 at "Neural Computing and Applications"
DOI: 10.1007/s00521-017-3145-y
Abstract: This paper describes the importance of performing preprocessing techniques namely, denoising and dimensionality reduction to the hyperspectral data before classification. The two main problems faced in hyperspectral image processing are noise and higher dimension. Legendre–Fenchel… read more here.
Keywords: technique; reduction; classification; hyperspectral image ... See more keywords
Local binary pattern-based discriminant graph construction for dimensionality reduction with application to face recognition
Sign Up to like & getrecommendations! Published in 2019 at "Multimedia Tools and Applications"
DOI: 10.1007/s11042-019-7518-3
Abstract: Graph construction has attracted increasing interest in recent years due to its key role in many dimensionality reduction (DR) algorithms. On the other hand, our previous study shows that the Local-Binary-Pattern Image (LBPI) representation is… read more here.
Keywords: discriminant graph; graph; graph construction; dimensionality reduction ... See more keywords
Multiview Classification and Dimensionality Reduction of Scalp and Intracranial EEG Data through Tensor Factorisation
Sign Up to like & getrecommendations! Published in 2018 at "Journal of Signal Processing Systems"
DOI: 10.1007/s11265-016-1164-z
Abstract: Electroencephalography (EEG) signals arise as mixtures of various neural processes which occur in particular spatial, frequency, and temporal brain locations. In classification paradigms, algorithms are developed that can distinguish between these processes. In this work,… read more here.
Keywords: tensor factorisation; eeg data; classification; dimensionality reduction ... See more keywords
Band selection using variational mode decomposition applied in sparsity-based hyperspectral unmixing algorithms
Sign Up to like & getrecommendations! Published in 2018 at "Signal, Image and Video Processing"
DOI: 10.1007/s11760-018-1301-1
Abstract: In this work, a frequency-based dimensionality reduction technique using variational mode decomposition (VMD) is proposed. Dimensionality reduction is a very important aspect of preprocessing in case of hyperspectral image (HSI) analysis where this step helps… read more here.
Keywords: variational mode; using variational; band; dimensionality reduction ... See more keywords
Feasibility of ANFIS towards multiclass event classification in PFBR considering dimensionality reduction using PCA
Sign Up to like & getrecommendations! Published in 2017 at "Annals of Nuclear Energy"
DOI: 10.1016/j.anucene.2016.09.015
Abstract: Abstract Nuclear power plant (NPP) such as Prototype Fast Breeder Reactor (PFBR), is a paradigm of complex engineering which is safety critical in nature. It sends copious plant signals to the main control room. An… read more here.
Keywords: event; pca; classification; dimensionality reduction ... See more keywords
A Quantitative Framework for Evaluating Single-Cell Data Structure Preservation by Dimensionality Reduction Techniques
Sign Up to like & getrecommendations! Published in 2020 at "Cell reports"
DOI: 10.1016/j.celrep.2020.107576
Abstract: SUMMARY High-dimensional data, such as those generated by single-cell RNA sequencing (scRNA-seq), present challenges in interpretation and visualization. Numerical and computational methods for dimensionality reduction allow for low-dimensional representation of genome-scale expression data for downstream… read more here.
Keywords: cell; dimensionality reduction; structure preservation;
Dimensionality reduction by UMAP reinforces sample heterogeneity analysis in bulk transcriptomic data.
Sign Up to like & getrecommendations! Published in 2021 at "Cell reports"
DOI: 10.1016/j.celrep.2021.109442
Abstract: Transcriptomic analysis plays a key role in biomedical research. Linear dimensionality reduction methods, especially principal-component analysis (PCA), are widely used in detecting sample-to-sample heterogeneity, while recently developed non-linear methods, such as t-distributed stochastic neighbor embedding… read more here.
Keywords: sample heterogeneity; analysis; bulk transcriptomic; dimensionality reduction ... See more keywords
Dimensionality reduction of germanium selenide for high-efficiency thermoelectric applications
Sign Up to like & getrecommendations! Published in 2019 at "Ceramics International"
DOI: 10.1016/j.ceramint.2019.04.253
Abstract: Abstract Dimensionality reduction has been proved as a feasible route to enhance the performance of thermoelectric materials for renewable energy applications. In this article, we investigate the effect of dimensions reduction on thermoelectric properties of… read more here.
Keywords: germanium selenide; gese gese; dimensionality reduction; reduction ... See more keywords
Characterizing the transient electrocardiographic signature of ischemic stress using Laplacian Eigenmaps for dimensionality reduction
Sign Up to like & getrecommendations! Published in 2020 at "Computers in biology and medicine"
DOI: 10.1016/j.compbiomed.2020.104059
Abstract: OBJECTIVE Despite a long history of ECG-based monitoring of acute ischemia quantified by several widely used clinical markers, the diagnostic performance of these metrics is not yet satisfactory, motivating a data-driven approach to leverage underutilized… read more here.
Keywords: laplacian eigenmaps; ischemia; dimensionality reduction; performance ... See more keywords
Dimensionality reduction for visualizing industrial chemical process data
Sign Up to like & getrecommendations! Published in 2019 at "Control Engineering Practice"
DOI: 10.1016/j.conengprac.2019.104189
Abstract: Abstract This paper explores dimensionality reduction (DR) approaches for visualizing high dimensional data in chemical processes. Visualization provides powerful insight and process understanding in the industrial context, and accelerates process troubleshooting. A diverse array of… read more here.
Keywords: reduction; reduction visualizing; process; dimensionality reduction ... See more keywords