Results 41 to 50 of about 8,023,314 (274)
Dimensionality reduction by LPP‐L21
IET Computer Vision, 2018 Locality preserving projection (LPP) is one of the most representative linear manifold learning methods and well exploits intrinsic structure of data. However, the performance of LPP remarkably degenerate in the presence of outliers.
Shujian Wang +3 more
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Dimensionality reduction in neuroscience [PDF]
Current Biology, 2016 The nervous system extracts information from its environment and distributes and processes that information to inform and drive behaviour. In this task, the nervous system faces a type of data analysis problem, for, while a visual scene may be overflowing with information, reaching for the television remote before us requires extraction of only a ...
Adrienne L. Fairhall +2 more
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Torsion in cohomology and dimensional reduction
Journal of High Energy Physics, 2023 Abstract Conventional wisdom dictates that ℤN factors in the integral cohomology group Hp(Xn, ℤ) of a compact manifold Xn cannot be computed via smooth p-forms. We revisit this lore in light of the dimensional reduction of string theory on Xn, endowed with a G-structure metric that leads to a supersymmetric EFT.
Gonzalo F. Casas +2 more
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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.
I. Jack, D.R.T. Jones, K. Roberts
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International Conference on Image and Signal Processing, 2020 Dimensionality reduction is widely used in machine learning and big data analytics since it helps to analyze and to visualize large, high-dimensional datasets.
M. Allaoui, M. L. Kherfi, A. Cheriet
semanticscholar +1 more source
Spontaneous dimensional reduction? [PDF]
AIP Conference Proceedings, 2012 To appear in Proc.
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Proximities in dimensionality reduction
, 2022 Dimensionality reduction aims at representing high-dimensional data in a lower-dimensional representation, while preserving their structure (clusters, outliers, manifold). Dimensionality reduction can be used for exploratory data visualization, data compression, or as a preprocessing to some other analysis in order to alleviate the curse of ...
Lee, John Aldo +3 more
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Factorization and regularization by dimensional reduction [PDF]
Physics Letters B, 2005 Since an old observation by Beenakker et al, the evaluation of QCD processes in dimensional reduction has repeatedly led to terms that seem to violate the QCD factorization theorem. We reconsider the example of the process gg->ttbar and show that the factorization problem can be completely resolved.
Signer, A., Stöckinger, D.
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Journal of Big Data, 2020 Telecom Companies logs customer’s actions which generate a huge amount of data that can bring important findings related to customer’s behavior and needs.
Maha Alkhayrat +2 more
semanticscholar +1 more source
NDDR-CNN: Layerwise Feature Fusing in Multi-Task CNNs by Neural Discriminative Dimensionality Reduction [PDF]
Computer Vision and Pattern Recognition, 2018 In this paper, we propose a novel Convolutional Neural Network (CNN) structure for general-purpose multi-task learning (MTL), which enables automatic feature fusing at every layer from different tasks.
Yuan Gao +5 more
semanticscholar +1 more source