On dimension reduction in Gaussian filters od 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. …”
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Sampling-based algorithms for dimension reduction od Deshpande, Amit Jayant

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Dimension reduction in recurrent networks by canonicalization od 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. …”
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Multifidelity Dimension Reduction via Active Subspaces od 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. …”
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Feature dimension reduction for microarray data analysis od Shi, Chao

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

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Likelihood-informed dimension reduction for nonlinear inverse problems od 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. …”
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Point cloud denoising by robust PCA dimension reduction od Guo, Yu

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Dimension reduction of streaming data via random projections od Cosma, I

Convergence rate of dimension reduction in Bose-Einstein condensates od Bao, W, Ge, Y, Jaksch, D, Markowich, P, Weishaeupl, R

“…In this paper, we study dimension reduction of the three-dimensional (3D) Gross-Pitaevskii equation (GPE) modeling Bose-Einstein condensation under different limiting interaction and trapping frequency parameter regimes. …”

Gradient-Based Dimension Reduction of Multivariate Vector-Valued Functions od 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.…”
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Gradient-Based Dimension Reduction of Multivariate Vector-Valued Functions od 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.…”
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Semi-supervised dimension reduction using trace ratio criterion od 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. …”
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Alternative Model for Extracting Multidimensional Data Based-on Comparative Dimension Reduction od 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. …”
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A time-triggered dimension reduction algorithm for the task assignment problem od Wang, H, Margellos, K, Papachristodoulou, A

“…The computational speed is accelerated via our time-triggered dimension reduction scheme, where the triggering condition is designed based on the optimality tolerance and the convexity of the cost function. …”

Improved interpretability of brain-behaviour CCA with domain-driven dimension reduction od Smith, SM, Liu, Z, Whittaker, K, Nichols, T

“…In this paper, we introduce a Domain-driven Dimension Reduction (DDR) method, reducing the dimensionality of the original datasets combining human knowledge of the structure of the variables studied. …”

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

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

“…Our final thrust broadens the applicability of gradient-based dimension reduction to problems where such gradients are not available. …”
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Dimension reduction algorithms for near-optimal low-dimensional embeddings and compressive sensing od Grant, Elyot

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Patch distribution compatible semisupervised dimension reduction for face and human gait recognition od 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. …”
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