Sentence examples for efficient dimension from inspiring English sources

In order to extract discriminating multi-voxel patterns from fMRI data, scalable, robust, and efficient dimension reduction tools are desired to identify influential voxels and voxel-based connectivity.

In other words, for the small efficient dimension, which is the sum of fractional derivatives of the equations in the systems such as system (2.1), the numerical results are not accurate enough.

Therefore, there is demand for efficient dimension reduction methods, both to provide high quality approximate numerical solutions to the stochastic evolution equations arising in high-dimensional systems, and to provide an efficient conceptual framework for interpretation of the behavior of such systems.

Moreover, due to the decomposition of the data into estimated latent variables, NPLSR can provide efficient dimension reduction possibilities in high-dimensional systems.

Moreover, ellipsoidFN, an efficient dimension reduction method to select a representative subset of PPIA, is utilized to perform the feature selection from high dimensional data.

A simple video model was then performed to extract the unknown optical parameters of the device such as the angular field of view, the efficient dimensions of each CCD array and the transfer function from the CCD to the TV screen.

Its aim is to detect medically- and socially-relevant differences in health status and changes in health status over time using a small number of statistically-efficient dimensions.

In the core of the stochastic structural analysis component of the proposed framework lies an efficient approximate dimension reduction technique based on the concepts of statistical linearization and of stochastic averaging for determining the non-stationary system response amplitude probability density functions (PDFs); thus, computationally intensive Monte Carlo simulations are circumvented.

In Whitehead's and Hartshorne's theism, there is an efficient causal dimension to God's creativity, but it is not deterministic.

Under the assumption that most features are not significant in high-dimensional data analysis, feature selection methods like Lasso (L1 penalty) and Elastic Nets (both L1 and L2 penalty) and their variants are often found useful and efficient in dimension reduction and feature selection.

Based on improved multi-objective particle swarm optimization (MOPSO) algorithm with principal component analysis (PCA) methodology, an efficient high-dimension multi-objective optimization method is proposed, which, as the purpose of this paper, aims to improve the convergence of Pareto front in multi-objective optimization design.