LDA works by projecting the data onto a lower-dimensional space with a criterion that maximizes the separation between multiple classes. It does this by finding the directions (linear discriminants) that maximize the ratio of between-class variance to within-class variance, thereby ensuring maximum class separability. In nanotechnology, this could mean distinguishing between different types of nanoparticles based on their size, shape, or surface properties.
