Results 11 to 20 of about 8,205,868 (397)
Supervised dimensionality reduction for big data
Nature Communications, 2021 Biomedical measurements usually generate high-dimensional data where individual samples are classified in several categories. Vogelstein et al. propose a supervised dimensionality reduction method which estimates the low-dimensional data projection for ...
Joshua T. Vogelstein +6 more
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Supersymmetry breaking by dimensional reduction over coset spaces [PDF]
Phys.Lett.B504:122-130,2001, 2001 We study the dimensional reduction of a ten-dimensional supersymmetric E_8 gauge theory over six-dimensional coset spaces. We find that the coset space dimensional reduction over a symmetric coset space leaves the four dimensional gauge theory without any track of the original supersymmetry.
P. Manousselis, George Zoupanos
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Genome Biology, 2019 Background Dimensionality reduction is an indispensable analytic component for many areas of single-cell RNA sequencing (scRNA-seq) data analysis. Proper dimensionality reduction can allow for effective noise removal and facilitate many downstream ...
Shiquan Sun +3 more
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Binary Whale Optimization Algorithm for Dimensionality Reduction
Mathematics, 2020 Feature selection (FS) was regarded as a global combinatorial optimization problem. FS is used to simplify and enhance the quality of high-dimensional datasets by selecting prominent features and removing irrelevant and redundant data to provide good ...
Abdelazim G. Hussien +4 more
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Dimensionality reduction with image data [PDF]
, 2004 A common objective in image analysis is dimensionality reduction. The most common often used data-exploratory technique with this objective is principal component analysis. We propose a new method based on the projection of the images as matrices after a
Benito Bonito, Mónica +1 more
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Exploring the Influence of Dimensionality Reduction on Anomaly Detection Performance in Multivariate Time Series [PDF]
IEEE AccessThis paper presents an extensive empirical study on the integration of dimensionality reduction techniques with advanced unsupervised time series anomaly detection models, focusing on the MUTANT and Anomaly-Transformer models.
Mahsun Altin, Altan Cakir
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Dimensional Reduction by Conformal Bootstrap [PDF]
Progress of Theoretical and Experimental Physics, 2018 The dimensional reductions in the branched polymer and the random field Ising model (RFIM) are discussed by a conformal bootstrap method. The small size minors are applied for the evaluations of the scale dimensions of these two models and the results are compared to D'=D-2 dimensional Yang-Lee edge singularity and to pure D'=D-2 dimensional Ising ...
arxiv +5 more sources
Ten quick tips for effective dimensionality reduction.
PLoS Computational Biology, 2019 Dimensionality reduction (DR) is frequently applied during the analysis of high-dimensional data. Both a means of denoising and simplification, it can be beneficial for the majority of modern biological datasets, in which it’s not uncommon to have ...
Lan Huong Nguyen, Susan Holmes
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Using Dimensionality Reduction to Analyze Protein Trajectories
Frontiers in Molecular Biosciences, 2019 In recent years the analysis of molecular dynamics trajectories using dimensionality reduction algorithms has become commonplace. These algorithms seek to find a low-dimensional representation of a trajectory that is, according to a well-defined ...
Gareth A. Tribello, Piero Gasparotto
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Dimensionality Reduction: Challenges and Solutions [PDF]
ITM Web of Conferences, 2022 The use of dimensionality reduction techniques is a keystone for analyzing and interpreting high dimensional data. These techniques gather several data features of interest, such as dynamical structure, input-output relationships, the correlation between
Ahmad Noor, Nassif Ali Bou
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