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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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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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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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Non-negative Matrix Factorization for Dimensionality Reduction [PDF]
ITM Web of Conferences, 2022 —What matrix factorization methods do is reduce the dimensionality of the data without losing any important information. In this work, we present the Non-negative Matrix Factorization (NMF) method, focusing on its advantages concerning other methods of ...
Olaya Jbari, Otman Chakkor
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Dimensional reduction of electromagnetism [PDF]
Journal of Mathematical Physics, 2022 We derive one- and two-dimensional models for classical electromagnetism by making use of Hadamard’s method of descent. Low-dimensional electromagnetism is conceived as a specialization of the higher-dimensional one, in which the fields are uniform along the additional spatial directions.
Rocco Maggi +5 more
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Modern Physics Letters A, 1999 Using an octonionic formalism, we introduce a new mechanism for reducing ten space–time dimensions to four without compactification. Applying this mechanism to the free, ten-dimensional, massless (momentum space) Dirac equation results in a particle spectrum consisting of exactly three generations.
Tevian Dray, Corinne A. Manogue
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Feature dimensionality reduction: a review
Complex & Intelligent Systems, 2022 As basic research, it has also received increasing attention from people that the “curse of dimensionality” will lead to increase the cost of data storage and computing; it also influences the efficiency and accuracy of dealing with problems.
Weikuan Jia +3 more
semanticscholar +1 more source