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Dimensionality reduction mappings [PDF]
2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), 2011 A wealth of powerful dimensionality reduction methods has been established which can be used for data visualization and preprocessing. These are accompanied by formal evaluation schemes, which allow a quantitative evaluation along general principles and which even lead to further visualization schemes based on these objectives.
Bunte, Kerstin +3 more
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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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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.
Manogue, Corinne A., Dray, Tevian
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DIMENSIONAL REDUCTION ON A SPHERE [PDF]
International Journal of Modern Physics B, 2006 The question of the dimensional reduction of two-dimensional (2d) quantum models on a sphere to one-dimensional (1d) models on a circle is addressed. A possible application is to look at a relation between the 2d anyon model and the 1d Calogero–Sutherland model, which would allow for a better understanding of the connection between 2d anyon exchange ...
Moller, Gunnar +2 more
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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 methods [PDF]
Advances in Methodology and Statistics, 2005 In case one or more sets of variables are available, the use of dimensional reduction methods could be necessary. In this contest, after a review on the link between the Shrinkage Regression Methods and Dimensional Reduction Methods, authors provide a different multivariate extension of the Garthwaite's PLS approach (1994) where a simple linear ...
D'AMBRA L, AMENTA P, GALLO, Michele
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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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