Linear discriminant analysis derives vectors from the principal components (PCs) and so minimises the within-category differences (which would mostly be associated with typical heterogeneity in any tissue sample) while maximising between-category discriminating characteristics (i.e., those most likely to be diagnostic).
DNA was obtained from 24 breeds of chicken [ 33] consisting of individuals from 3 or 4 flocks to maximise between- and within-breed genetic diversity.
Kernel discriminant analysis, which employs the kernel technique to perform linear discriminant analysis in a high-dimensional feature space, is developed to extract the significant nonlinear features which maximise the between-class variance and minimise the within-class variance.
The objective function in our case consists of: i) maximising correlation between λ i and envirome variables (or equivalently between r(t) and x t)), and ii) minimising redundancy, i.e. eliminating all elementary modes with weak correlations with the envirome.
"They score well in terms of pollution abatement and efficient use of natural resources, while also responding favourably to the demographic challenges caused by a rising population (eg improving local air quality and reducing congestion)." He has invested in Kansas City Southern, as well as Hub Group, which, like JB Hunt, is an "intermodal" company maximising efficiency between road and rail.
As part of the NFL's long-established policy of maximising competition between its teams, the Draft order is determined by how teams fared in the previous season.
Each trial contributed a single estimate to each outcome category, with decisions about inclusion of multiple outcomes based on maximising comparability between studies.
Traditional cluster analysis methods initially create a distance measure of dissimilarity between individuals (such as a Euclidean distance), and then seek to determine the underlying subgroup structure by optimising the within-subgroup variability of individuals' distance measures and maximising their between group variability.
This grouping method is based on the ArcGIS implementation of the Fisher-Jenks algorithm and minimizes differences between data values within classes and maximises differences between classes [ 24, 25].
He thinks that useful languages should allot words in order to minimise the perceptual difference between colours of the same category, and maximise it between colours in different categories.
