Figure 12. Encoder networks for nonlinear mapping of high-dimensional data.

Figure 12

Encoder networks for nonlinear mapping of high-dimensional data. Neurons are drawn as circles, weights are represented by lines. Input neurons (white) are fan-out units, hidden-layer units (black) have a sigmoidal or linear activity, and the output neurons (gray) are linear

a) symmetrical network architecture attempting to reproduce the input patterns by going through a low-dimensional internal representation. Factor 1 and Factor 2 are the score values (co-ordinates) in the low-dimensional (here: two-dimensional) map.

b) conventional feed-forward network with two output neurons. The outputs represent the low-dimensional scores.

From: Analysis of Chemical Space

Cover of Madame Curie Bioscience Database
Madame Curie Bioscience Database [Internet].
Austin (TX): Landes Bioscience; 2000-2013.
Copyright © 2000-2013, Landes Bioscience.

NCBI Bookshelf. A service of the National Library of Medicine, National Institutes of Health.