Visualizing non-metric similarities in multiple maps

• Published in: Machine learning Vol. 87; no. 1; pp. 33 - 55
• Main Authors: van der Maaten, Laurens, Hinton, Geoffrey
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.04.2012; Springer Nature B.V
• Subjects: Analogies; Artificial Intelligence; Collection; Computer Science; Construction; Control; Data analysis; Embedded systems; Machine learning; Mechatronics; Metric space; Metric system; Natural Language Processing (NLP); Robotics; Simulation and Modeling; Visualization; Embedding; Non-metric similarities; Multidimensional scaling; Data visualization
• ISSN: 0885-6125; 1573-0565
• DOI: 10.1007/s10994-011-5273-4

Techniques for multidimensional scaling visualize objects as points in a low-dimensional metric map. As a result, the visualizations are subject to the fundamental limitations of metric spaces. These limitations prevent multidimensional scaling from faithfully representing non-metric similarity data such as word associations or event co-occurrences. In particular, multidimensional scaling cannot faithfully represent intransitive pairwise similarities in a visualization, and it cannot faithfully visualize “central” objects. In this paper, we present an extension of a recently proposed multidimensional scaling technique called t-SNE. The extension aims to address the problems of traditional multidimensional scaling techniques when these techniques are used to visualize non-metric similarities. The new technique, called multiple maps t-SNE, alleviates these problems by constructing a collection of maps that reveal complementary structure in the similarity data. We apply multiple maps t-SNE to a large data set of word association data and to a data set of NIPS co-authorships, demonstrating its ability to successfully visualize non-metric similarities.