Methods Based On Proximity Matrices
A method based on proximity matrices is one where the data is presented to the algorithm in the form of a similarity matrix or a distance matrix. These methods all fall under the broader class of metric multidimensional scaling. The variations tend to be differences in how the proximity data is computed; for example, Isomap, locally linear embeddings, maximum variance unfolding, and Sammon mapping (which is not in fact a mapping) are examples of metric multidimensional scaling methods.
Read more about this topic: Nonlinear Dimensionality Reduction
Famous quotes containing the words methods, based and/or proximity:
“Commerce is unexpectedly confident and serene, alert, adventurous, and unwearied. It is very natural in its methods withal, far more so than many fantastic enterprises and sentimental experiments, and hence its singular success.”
—Henry David Thoreau (1817–1862)
“Both magic and religion are based strictly on mythological tradition, and they also both exist in the atmosphere of the miraculous, in a constant revelation of their wonder-working power. They both are surrounded by taboos and observances which mark off their acts from those of the profane world.”
—Bronislaw Malinowski (1884–1942)
“Our senses perceive no extreme. Too much sound deafens us; too much light dazzles us; too great distance or proximity hinders our view. Too great length and too great brevity of discourse tends to obscurity; too much truth is paralyzing.... In short, extremes are for us as though they were not, and we are not within their notice. They escape us, or we them.”
—Blaise Pascal (1623–1662)