Results 41 to 50 of about 8,205,868 (397)
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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Dimensionality reduction of clustered data sets [PDF]
, 2008 We present a novel probabilistic latent variable model to perform linear dimensionality reduction on data sets which contain clusters. We prove that the maximum likelihood solution of the model is an unsupervised generalisation of linear discriminant ...
Sanguinetti, G.
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Modular Dimensionality Reduction [PDF]
, 2019 We introduce an approach to modular dimensionality reduction, allowing efficient learning of multiple complementary representations of the same object. Modules are trained by optimising an unsupervised cost function which balances two competing goals: Maintaining the inner product structure within the original space, and encouraging structural ...
Reeve, Henry W J +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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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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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
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
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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