Sentence examples for much lower dimensional from inspiring English sources

Word embedding creates a vector representation of words with a much lower dimensional space compared to the bag of the words model [53, 54].

On the one hand, microarray and other gene expression experiments typically generate high-dimensional data in the form of a real vector that comprises expression levels of multiple genes at each sampled time and/or condition point, whereas fitness measurements map the state of the system into a much lower dimensional space e.g. that of a single real variable, such as growth rate.

SOM provides a technique for representation of multidimensional data into much lower-dimensional spaces (usually one or two dimensions).

The basic idea behind topic modeling is the mapping of high-dimensional representation of images in the form of BoVW to a much lower-dimensional space defined by the latent topics.

Dynamical systems analysis however shows that the long-term behaviour of chemical systems is usually restricted to much lower-dimensional manifolds in the total species space, due to many of the fast time-scales quickly reaching local equilibrium.

This may not only lead to possible confusion in interpreting the images, but more critically may also suggest that the reconstruction algorithm is failing to completely exploit the joint structure of the ensemble-the images are in fact constrained to live in a much lower-dimensional set than the algorithm realizes.

In unsupervised clustering in high dimensions, feature selection is likewise essential for discerning the underlying grouping tendency that may be 'buried' in a much lower-dimensional subspace – with many structurally irrelevant features and only a small sample size, clustering algorithms are likely to identify false group structure.

High-dimensional data contained in the patent documents are transformed into much lower dimensionality space (2D or 3D), clustered and visualized.

Their induced decreases in the elastic moduli and strengths are found to be much lower than those of two dimensional (2D) foams and Kagome grids.

The simulation results show that our proposed 2D measurement matrix design using gradient decent algorithm (2D-MMDGD) has much lower computational complexity compared to one-dimensional (1D) methods while having better performance in comparison with conventional methods such as Gaussian random measurement matrix.

Classical PCA is a linear transform that maps the data into a lower dimensional space by preserving as much data variance as possible.