On dimension reduction in Gaussian filters by Hakkarainen, Janne, Solonen, Antti, Cui, Tiangang, Marzouk, Youssef M

“…A priori dimension reduction is a widely adopted technique for reducing the computational complexity of stationary inverse problems. …”
Get full text
Get full text
Get full text
Get full text

Sampling-based algorithms for dimension reduction by Deshpande, Amit Jayant

Get full text

Dimension reduction in recurrent networks by canonicalization by Grigoryeva, Lyudmila, Ortega, Juan-Pablo

“…The classical notion of canonical state-space realization is adapted in this paper to accommodate semi-infinite inputs so that it can be used as a dimension reduction tool in the recurrent networks setup. …”
Get full text

Multifidelity Dimension Reduction via Active Subspaces by Lam, Remi R, Zahm, Olivier, Marzouk, Youssef M, Willcox, Karen E

“…We propose a multifidelity dimension reduction method to identify a low-dimensional structure present in many engineering models. …”
Get full text

Feature dimension reduction for microarray data analysis by Shi, Chao

Get full text

Dimension reduction for semidefinite programs via Jordan algebras by Permenter, Frank Noble, Parrilo, Pablo A.

Get full text

Likelihood-informed dimension reduction for nonlinear inverse problems by Martin, J., Cui, Tiangang, Marzouk, Youssef M., Solonen, Antti, Spantini, Alessio

“…From a Bayesian perspective, changes from the prior to the posterior may, in many problems, be confined to a relatively low-dimensional subspace of the parameter space. We present a dimension reduction approach that defines and identifies such a subspace, called the 'likelihood-informed subspace' (LIS), by characterizing the relative influences of the prior and the likelihood over the support of the posterior distribution. …”
Get full text
Get full text
Get full text
Get full text
Get full text

Point cloud denoising by robust PCA dimension reduction by Guo, Yu

Get full text

Gradient-Based Dimension Reduction of Multivariate Vector-Valued Functions by Zahm, Olivier, Constantine, Paul G, Prieur, Clémentine, Marzouk, Youssef M

“…A numerical illustration shows that using gradients of the function yields effective dimension reduction. We also show how the choice of norm on the codomain of the function has an impact on the function's low-dimensional approximation.…”
Get full text

Gradient-Based Dimension Reduction of Multivariate Vector-Valued Functions by Zahm, Olivier, Constantine, Paul G, Prieur, Clémentine, Marzouk, Youssef M

“…A numerical illustration shows that using gradients of the function yields effective dimension reduction. We also show how the choice of norm on the codomain of the function has an impact on the function's low-dimensional approximation.…”
Get full text

Semi-supervised dimension reduction using trace ratio criterion by Huang, Yi, Xu, Dong, Nie, Feiping

“…In this brief, we address the trace ratio (TR) problem for semi-supervised dimension reduction. We first reformulate the objective function of the recent work semi-supervised discriminant analysis (SDA) in a TR form. …”
Get full text
Get full text

Alternative Model for Extracting Multidimensional Data Based-on Comparative Dimension Reduction by Sembiring, Rahmat Widia, Jasni, Mohamad Zain, Abdullah, Embong

“…In line with the technological developments, the current data tends to be multidimensional and high dimensional, which is more complex than conventional data and need dimension reduction. Dimension reduction is important in cluster analysis and creates a new representation for the data that is smaller in volume and has the same analytical results as the original representation. …”
Get full text

Scaling Bayesian optimization for engineering design : lookahead approaches and multifidelity dimension reduction by Lam, Remi Roger Alain Paul

Get full text

Gradient-based dimension reduction for Bayesian inverse problems and simulation-based inference by Brennan, Michael Cian

“…Our final thrust broadens the applicability of gradient-based dimension reduction to problems where such gradients are not available. …”
Get full text
Get full text

Dimension reduction algorithms for near-optimal low-dimensional embeddings and compressive sensing by Grant, Elyot

Get full text

Patch distribution compatible semisupervised dimension reduction for face and human gait recognition by Huang, Yi, Xu, Dong, Nie, Feiping

“…We propose a new semisupervised learning algorithm, referred to as patch distribution compatible semisupervised dimension reduction, for face and human gait recognition. …”
Get full text
Get full text

Beyond dimension reduction: Stable electric fields emerge from and allow representational drift by Pinotsis, Dimitris A, Miller, Earl K

Get full text

Enabling Efficient Uncertainty Quantification of Turbulent Combustion Simulations via Kinetic Dimension Reduction by Koenig, Benjamin C.

“…Various surrogate model and dimension reduction techniques have previously been applied in order to reduce the cost of forward uncertainty propagation in combustion simulations, but these are often limited to low-dimensional, simple combustion cases with scalar solution targets. …”
Get full text
Get full text

Social spider optimisation algorithm for dimension reduction of electroencephalogram signals in human emotion recognition by Al-Qammaz, Abdullah Yousef, Ahmad, Farzana Kabir, Yusof, Yuhanis

Get full text

Cross-Entropy-Based Importance Sampling with Failure-Informed Dimension Reduction for Rare Event Simulation by Uribe, Felipe, Papaioannou, Iason, Marzouk, Youssef M, Straub, Daniel

Get full text