Tapis kalman ruang waktu=Space time Kalman filter de réir Perpustakaan UGM, i-lib

“…This paper presents an approach to space time prediction that achieves dimension reduction and uses a statistical model that is temporally dynamic and spatially descriptive, called space time Kalman filter. …”

Feature Selection And Enhanced Krill Herd Algorithm For Text Document Clustering de réir Abualigah, Laith Mohammad Qasim

“…In this study, a new method for solving the TD clustering problem worked in the following two stages: (i) A new feature selection method using particle swarm optimization algorithm with a novel weighting scheme and a detailed dimension reduction technique are proposed to obtain a new subset of more informative features with low-dimensional space.…”
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Model selection in equations with many 'small' effects de réir Castle, J, Hendry, D

“…Automatic model selection procedures can handle perfect collinearity and more candidate variables than observations, allowing substantial dimension reduction from GUMs with salient regressors, lags, non-linear transformations, and multiple location shifts, together with all the principal components representing 'factor' structures, which can also capture small influences that selection may not retain individually. …”

Extreme learning machines for feature learning de réir Liyanaarachchi Lekamalage, Chamara Kasun

“…For dimension reduction the efficacy of linear ELM-AE, non-linear ELM-AE, linear SELM-AE, and non-linear SELM-AE is compared with PCA, Non-negative Matrix Factorization (NMF), and Tied weight Auto-Encoder (TAE) in terms of discriminative capability, sparsity, Normalized Mean Square Error (NMSE), and training time. …”
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Scalable posterior approximations for large-scale Bayesian inverse problems via likelihood-informed parameter and state reduction de réir Cui, Tiangang, Marzouk, Youssef M, Willcox, Karen E

“…As a byproduct of state dimension reduction, we also show how to identify low-dimensional subspaces of the data in problems with high-dimensional observations. …”
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Unsupervised clustering algorithms for flow/mass cytometry data de réir Koh, Kavan Li Wenn

“…This Final Year Project report documents the process of using dimension reduction and unsupervised clustering methods for clustering similar group of cells and to automate the discovery of cell populations from data sets generated from mass cytometry. …”
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Model selection in equations with many 'small' effects de réir Castle, J, Doornik, J, Hendry, D

“…Automatic modelselection procedures can handle more candidate variables than observations, allowing substantial dimension reduction from GUMs with salient regressors, lags, nonlinear transformations, and multiple location shifts, together with all the principal components, possibly representing ‘factor’ structures, as perfect collinearity is also unproblematic. …”

Model Selection in Equations with Many 'Small' Effects. de réir Castle, J, Doornik, J, Hendry, D

“…Automatic model selection procedures can handle perfect collinearity and more candidate variables than observations, allowing substantial dimension reduction from GUMs with salient regressors, lags, non-linear transformations, and multiple location shifts, together with all the principal components representing ‘factor’ structures, which can also capture small influences that selection may not retain individually. …”

Fast and accurate randomized algorithms for linear systems and eigenvalue problems de réir Nakatsukasa, Y, Tropp, JA

“…These algorithms apply fast randomized dimension reduction (``sketching"") to accelerate standard subspace projection methods, such as GMRES and Rayleigh--Ritz. …”

Analisis komponen utama citra aras-keabuan dengan jaringan syaraf tiruan de réir Perpustakaan UGM, i-lib

“…Principal components analysis allows to reduce the dimensionality of a dataset in which there are large number of interelated variabels, while retaining as much as posible of the variation present in dataset. This dimension reduction is achieved by transforming to a new set of variabels that have new meaning, called principal components, which are highly uncorrelated and which are ordered so that the first few retain most of the variation present in all of the original variabels. …”

Goods versus characteristics: revealed preference procedures for nested models de réir Polisson, M

“…The primary result is that the better fit of the characteristics model is entirely attributable to dimension reduction.…”

Factor high-frequency based volatility (HEAVY) models de réir Sheppard, K, Xu, W

“… We propose a new class of multivariate volatility models utilizing realized measures of asset volatility and covolatility extracted from high-frequency data. Dimension reduction for estimation of large covariance matrices is achieved by imposing a factor structure with time-varying conditional factor loadings. …”

Sparse F-IncSFA for action recognition de réir Loo, C., Bardia, Y.

“…It has revealed excellent and promising dimension reduction by this preprocessor. …”
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Face identification and verification using PCA and LDA de réir Chan, Lih Heng, Shaikh Salleh, Sheikh Hussain, Ting, Chee Ming, Ariff, Ahmad Kamarul

“…PCA is recognized as an optimal method to perform dimension reduction, yet being claimed as lacking discrimination ability. …”
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Reliable Real-Time Optimization of Nonconvex Systems Described by Parametrized Partial Differential Equations de réir Oliveira, I.B., Patera, Anthony T.

“…The critical ingredients of the method are: (i) reduced-basis techniques for dimension reduction in computational requirements; (ii) an "off-line/on-line" computational decomposition for the rapid calculation of outputs of interest and respective sensitivities in the limit of many queries; (iii) a posteriori error bounds for rigorous uncertainty and feasibility control; (iv) Interior Point Methods (IPMs) for efficient solution of the optimization problem; and (v) a trust-region Sequential Quadratic Programming (SQP) interpretation of IPMs for treatment of possibly non-convex costs and constraints.…”
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Improved nu-support vector regression algorithm based on principal component analysis de réir Abdullah Mohammed, Rashid, Habshah, Midi

“…Principal component analysis (PCA) is the most commonly used approach for analysing high-dimensional data in order to achieve dimension reduction. However, outliers have an adverse effect on the PCA, and hence reduce the accuracy of the prediction model. …”

Pricing variance swaps under stochastic volatility and stochastic interest rate de réir Cao, Jiling, Lian, Guanghua, Roslan, Teh Raihana Nazirah

“…In this paper, we investigate the effects of imposing stochastic interest rate driven by the Cox–Ingersoll–Ross process along with the Heston stochastic volatility model for pricing variance swaps with discrete sampling times. A dimension reduction mechanism based on the framework of Little and Pant (2001) is applied which later reduces to solving two three-dimensional partial differential equations. …”
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Further insights into subspace methods with applications in face recognition de réir Zhu, Yan

“…Subspace methods such as Linear Discriminant Analysis (LDA) are efficient in dimension reduction and statistical feature extraction. …”
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The effectiveness of applying machine learning to forecast the Singapore economy de réir Foo, Benedict, Koh, Deng Yao, Tan, Juan Pang

“…For the VAR model, we use penalized regression techniques such as the least absolute shrinkage and selection operator (LASSO), the elastic net (ENET), and the group LASSO (GLASSO) to achieve dimension reduction. For the DI model, we combine it with cross-validation and LASSO techniques for the selection of the number of factors. …”
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Identification of epilepsy utilizing hilbert transform and SVM based classifier de réir Talal M Bakhsh, Saeed Meshgini, Ali Farzamnia

“…Then, after the PCA dimension reduction a two-class SVM classifier is used for EEG signals automatic classification, one class for healthy subjects and another for subjects with epilepsy. …”
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