Results 21 to 30 of about 8,013,557 (321)

Applications and Comparison of Dimensionality Reduction Methods for Microbiome Data

Frontiers in Bioinformatics, 2022
Dimensionality reduction techniques are a key component of most microbiome studies, providing both the ability to tractably visualize complex microbiome datasets and the starting point for additional, more formal, statistical analyses. In this review, we
George Armstrong, Gibraan Rahman, C. Martino, Daniel McDonald, Antonio Gonzalez, Gal Mishne, R. Knight   +6 more
semanticscholar   +1 more source

Equivalence of dimensional reduction and dimensional regularisation [PDF]

Zeitschrift für Physik C Particles and Fields, 1994
For some years there has been uncertainty over whether regularisation by dimensional reduction (DRED) is viable for non-supersymmetric theories. We resolve this issue by showing that DRED is entirely equivalent to standard dimensional regularisation (DREG), to all orders in perturbation theory and for a general renormalisable theory.
Jack, I., Jones, D. R. T., Roberts, K. L.   +2 more
openaire   +2 more sources

Interactive Visual Cluster Analysis by Contrastive Dimensionality Reduction

IEEE Transactions on Visualization and Computer Graphics, 2022
We propose a contrastive dimensionality reduction approach (CDR) for interactive visual cluster analysis. Although dimensionality reduction of high-dimensional data is widely used in visual cluster analysis in conjunction with scatterplots, there are ...
Jiazhi Xia, Linquan Huang, W. Lin, Xin Zhao, Jing Wu, Yang Chen, Ying Zhao, Wei Chen   +7 more
semanticscholar   +1 more source

Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer

Nature Communications, 2021
High-dimensional multi-omics data are now standard in biology. They can greatly enhance our understanding of biological systems when effectively integrated.
Laura Cantini, Pooya Zakeri, Céline Hernandez, A. Naldi, D. Thieffry, É. Remy, A. Baudot   +6 more
semanticscholar   +1 more source

Evaluating dimensionality reduction for genomic prediction

Frontiers in Genetics, 2022
The development of genomic selection (GS) methods has allowed plant breeding programs to select favorable lines using genomic data before performing field trials.
Vamsi Manthena, Diego Jarquín, Rajeev K. Varshney, Rajeev K. Varshney, Manish Roorkiwal, Manish Roorkiwal, Girish Prasad Dixit, Chellapilla Bharadwaj, Reka Howard   +8 more
doaj   +1 more source

Dimensionality reduction by UMAP reinforces sample heterogeneity analysis in bulk transcriptomic data

bioRxiv, 2021
Transcriptome profiling and differential gene expression constitute a ubiquitous tool in biomedical research and clinical application. Linear dimensionality reduction methods especially principal component analysis (PCA) are widely used in detecting ...
Yang Yang, Hongjian Sun, Yu Zhang, Tiefu Zhang, Jialei Gong, Yunbo Wei, Y. Duan, M. Shu, Yuchen Yang, Di Wu, Di Yu   +10 more
semanticscholar   +1 more source

2D Dimensionality Reduction Methods without Loss [PDF]

Journal of Artificial Intelligence and Data Mining, 2019
In this paper, several two-dimensional extensions of principal component analysis (PCA) and linear discriminant analysis (LDA) techniques has been applied in a lossless dimensionality reduction framework, for face recognition application.
S. Ahmadkhani, P. Adibi, A. ahmadkhani
doaj   +1 more source

Analyzing Quality Measurements for Dimensionality Reduction

Machine Learning and Knowledge Extraction, 2023
Dimensionality reduction methods can be used to project high-dimensional data into low-dimensional space. If the output space is restricted to two dimensions, the result is a scatter plot whose goal is to present insightful visualizations of distance ...
Michael C. Thrun, Julian Märte, Quirin Stier   +2 more
doaj   +1 more source

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

Journal of Applied Science and Technology Trends, 2020
Due to sharp increases in data dimensions, working on every data mining or machine learning (ML) task requires more efficient techniques to get the desired results.
R. Zebari, A. Abdulazeez, Diyar Qader Zeebaree, D. A. Zebari, J. Saeed   +4 more
semanticscholar   +1 more source

Analysis of Dimensionality Reduction Techniques on Big Data

IEEE Access, 2020
Due to digitization, a huge volume of data is being generated across several sectors such as healthcare, production, sales, IoT devices, Web, organizations. Machine learning algorithms are used to uncover patterns among the attributes of this data. Hence,
G. T. Reddy, M. P. K. Reddy, Kuruva Lakshmanna, Rajesh Kaluri, D. Rajput, Gautam Srivastava, Ieee Thar Baker Senior Member, Gautam Srivastava   +7 more
semanticscholar   +1 more source