If many American musicals these days seem to turn their inhabitants into cartoons, this show, inspired by a comic-strip-style book, has paradoxically created characters who are highly multidimensional.
Such models often live in highly multidimensional spaces suffering the so-called curse of dimensionality.
However, such models result defined in highly multidimensional spaces suffering the so-called curse of dimensionality.
However, such models, that must be solved off-line, are defined in highly multidimensional spaces suffering the so-called curse of dimensionality.
The training problem of the neuro-fuzzy architecture has been configured as a highly multidimensional stochastic global optimization problem and improved variants of particle swarm optimization (PSO) techniques have been successfully implemented for it.
PCA reduces our highly multidimensional data set by generating weighted (or loaded) linear combinations [i.e., principal components (PCs)] of the metagenomic categories (e.g., ARGs, MRGs).
A further aspect of interest in highly multidimensional data is connectivity, i.e., how much inter correlated are the different variable, here expression levels.
A large amount of data has already been acquired and is highly multidimensional and multimodal; therefore it is the analysis that remains the challenge.
In addition, other computational factors such as finding the global optimal solution over highly multidimensional parameter space and integrating stiff ODEs, often make the parameter estimation numerically intractable, even for models with 10 20 parameters [ 5].
Not to mention, finding the global minimum of model residuals over highly multidimensional parameter space is challenging and can become prohibitively expensive to perform on a computer workstation, even for tens of parameters.
For ordinary differential equation (ODE) models, the challenge in this estimation has been attributed to not only the lack of parameter identifiability, but also computational issues such as finding globally optimal parameter estimates over highly multidimensional search space.
