Results 21 to 30 of about 647,667 (335)

Analyzing Quality Measurements for Dimensionality Reduction

open access: yesMachine Learning and Knowledge Extraction, 2023
Dimensionality reduction methods can be used to project high-dimensional data into low-dimensional space. If the output space is restricted to two dimensions, the result is a scatter plot whose goal is to present insightful visualizations of distance ...
Michael C. Thrun   +2 more
doaj   +1 more source

An Orthogonal Locality and Globality Dimensionality Reduction Method Based on Twin Eigen Decomposition

open access: yesIEEE 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
doaj   +1 more source

Adaptive slope reliability analysis method based on sliced inverse regression dimensionality reduction

open access: yesFrontiers 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 for Handwritten Digit Recognition [PDF]

open access: yesEAI Endorsed Transactions on Cloud Systems, 2018
Human perception of dimensions is usually limited to two or three degrees. Any further increase in the number of dimensions usually leads to the difficulty in visual imagination for any person.
Ankita Das   +2 more
doaj   +1 more source

Effective and efficient approach in IoT Botnet detection

open access: yesJurnal 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

Dimensionality Reduction Mappings [PDF]

open access: yes, 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 ...
Biehl, Michael   +3 more
core   +2 more sources

Uniform Manifold Approximation and Projection (UMAP) Reveals Composite Patterns and Resolves Visualization Artifacts in Microbiome Data

open access: yesmSystems, 2021
Microbiome data are sparse and high dimensional, so effective visualization of these data requires dimensionality reduction. To date, the most commonly used method for dimensionality reduction in the microbiome is calculation of between-sample microbial ...
George Armstrong   +6 more
doaj   +1 more source

Dimensionality reduction in neuroscience [PDF]

open access: yesCurrent 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
openaire   +3 more sources

Torsion in cohomology and dimensional reduction

open access: yesJournal 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
openaire   +4 more sources

Modular Dimensionality Reduction [PDF]

open access: yes, 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
openaire   +5 more sources

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