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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 Mappings [PDF]
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
Dimensionality Reduction for Handwritten Digit Recognition [PDF]
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
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
Modular Dimensionality Reduction [PDF]
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
Dimensionality reduction of clustered data sets [PDF]
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.
core +2 more sources
Torsion in cohomology and dimensional reduction
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
Dimensionality reduction by LPP‐L21
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
doaj +1 more source
Manifold Elastic Net: A Unified Framework for Sparse Dimension Reduction [PDF]
It is difficult to find the optimal sparse solution of a manifold learning based dimensionality reduction algorithm. The lasso or the elastic net penalized manifold learning based dimensionality reduction is not directly a lasso penalized least square ...
A D’aspremont+40 more
core +1 more source
Dimensionality reduction in neuroscience [PDF]
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