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IEEE Access, 2021 Dimensionality reduction is a hot research topic in pattern recognition. Traditional dimensionality reduction methods can be separated into linear dimensionality reduction methods and nonlinear dimensionality reduction methods.
Shuzhi Su, Gang Zhu, Yanmin Zhu
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Frontiers in Ecology and Evolution, 2023 The response surface model has been widely used in slope reliability analysis owing to its efficiency. However, this method still has certain limitations, especially the curse of high dimensionality when considering the spatial variability of ...
Zheng Zhou +12 more
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Dimensionality Reduction with Nvidia Tensor Cores [PDF]
, 2022 openThanks to the popularity and effectiveness of machine learning, the computational requirements for its development have increased beyond the limits of conventional devices.
BALZAN, PIETRO
core
Effective and efficient approach in IoT Botnet detection
Jurnal Ilmiah SINERGI, 2023 Internet of Things (IoT) technology presents an advantage to daily life, but this advantage is not a guarantee of security. This is because cyber-attacks, such as botnets, remain a threat to the user.
Susanto Susanto +4 more
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On the dimensional reduction procedure [PDF]
Nuclear Physics B, 2001 15 pages, Latex, enlarged discussion added in Sec 3 and typos corrected. Version to appear in Nucl.
Cognola, Guido, Zerbini, Sergio
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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 ...
Henry W. J. Reeve +2 more
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Linear Dimensionality Reduction
CoRR, 2022 These notes are an overview of some classical linear methods in Multivariate Data Analysis. This is a good old domain, well established since the 60's, and refreshed timely as a key step in statistical learning. It can be presented as part of statistical learning, or as dimensionality reduction with a geometric flavor.
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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.
Jack, I. +2 more
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Nonlinear dimensionality reduction on graphs [PDF]
2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2017 In this era of data deluge, many signal processing and machine learning tasks are faced with high-dimensional datasets, including images, videos, as well as time series generated from social, commercial and brain network interactions. Their efficient processing calls for dimensionality reduction techniques capable of properly compressing the data while
Yanning Shen +2 more
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New approaches on dimensionality reduction in hyperspectral images for classification purposes [PDF]
, 2012 This paper presents a quasi-unsupervised methodology to detect endmembers within an hyperspectral scene and to derive a pixel-wise classification on its basis.
Rupert Mueller +7 more
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