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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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Correlated clustering and projection for dimensionality reduction [PDF]
Machine Learning: Science and TechnologyMost dimensionality reduction methods employ frequency domain representations obtained from matrix diagonalization and may not be efficient for large datasets with relatively high intrinsic dimensions. To address this challenge, correlated clustering and
Yuta Hozumi, Rui Wang, Guo-Wei Wei
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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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Dimensionality reduction of complex dynamical systems
iScience, 2021 Summary: One of the outstanding problems in complexity science and engineering is the study of high-dimensional networked systems and of their susceptibility to transitions to undesired states as a result of changes in external drivers or in the ...
Chengyi Tu +2 more
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Dimensionality reduction using singular vectors
Scientific Reports, 2021 A common problem in machine learning and pattern recognition is the process of identifying the most relevant features, specifically in dealing with high-dimensional datasets in bioinformatics.
Majid Afshar, Hamid Usefi
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Shape-aware stochastic neighbor embedding for robust data visualisations
BMC Bioinformatics, 2022 Background The t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm has emerged as one of the leading methods for visualising high-dimensional (HD) data in a wide variety of fields, especially for revealing cluster structure in HD single-cell ...
Tobias Wängberg +2 more
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Dimensionality reduction in Bayesian estimation algorithms [PDF]
Atmospheric Measurement Techniques, 2013 An idealized synthetic database loosely resembling 3-channel passive microwave observations of precipitation against a variable background is employed to examine the performance of a conventional Bayesian retrieval algorithm.
G. W. Petty
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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
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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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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 +8 more
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