Visualizing Similarity Data with a Mixture of Maps

James Cook, Ilya Sutskever, Andriy Mnih, Geoffrey Hinton; JMLR W&P 2:67-74, 2007.

Abstract

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.