Paper
Visualizing Similarity Data with a Mixture of Maps
We show how to visualize a set of pairwise similarities between objects by using several different two-dimensional maps, each of which captures different aspects of the similarity structure. When the objects are ambiguous words, for example, different senses of a word occur in different maps, so “river ” and “loan ” can both be close to “bank ” without being at all close to each other. Aspect maps resemble clustering because they model pair-wise similarities as a mixture of different types of similarity, but they also resemble local multi-dimensional scaling because they model each type of similarity by a twodimensional map. We demonstrate our method on a toy example, a database of human wordassociation data, a large set of images of handwritten digits, and a set of feature vectors that represent words. 1
Authors: James N. Cook · Ilya Sutskever · Andriy Mnih · Geoffrey E. Hinton