Results 31 to 40 of about 139,695 (308)
Nonlinear dimensionality reduction for clustering [PDF]
Pattern Recognition, 2020 Abstract We introduce an approach to divisive hierarchical clustering that is capable of identifying clusters in nonlinear manifolds. This approach uses the isometric mapping (Isomap) to recursively embed (subsets of) the data in one dimension, and then performs a binary partition designed to avoid the splitting of clusters.
Sotiris K. Tasoulis +2 more
openaire +1 more source
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
doaj +1 more source
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
doaj +1 more source
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
openaire +4 more sources
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
openaire +4 more sources
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.
openaire +2 more sources
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
openaire +2 more sources
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
openaire +2 more sources
Adaptive Metric Dimensionality Reduction [PDF]
Theoretical Computer Science, 2013 We study adaptive data-dependent dimensionality reduction in the context of supervised learning in general metric spaces. Our main statistical contribution is a generalization bound for Lipschitz functions in metric spaces that are doubling, or nearly doubling.
Lee-Ad Gottlieb +2 more
openaire +2 more sources