Maaten, L. van den; Hinton, G.: Visualizing non-metric similarities in multiple maps (2012)
0.02
0.020214936 = product of:
0.04042987 = sum of:
0.04042987 = product of:
0.08085974 = sum of:
0.08085974 = weight(_text_:t in 3884) [ClassicSimilarity], result of:
0.08085974 = score(doc=3884,freq=6.0), product of:
0.17876579 = queryWeight, product of:
3.9394085 = idf(docFreq=2338, maxDocs=44218)
0.04537884 = queryNorm
0.45232224 = fieldWeight in 3884, product of:
2.4494898 = tf(freq=6.0), with freq of:
6.0 = termFreq=6.0
3.9394085 = idf(docFreq=2338, maxDocs=44218)
0.046875 = fieldNorm(doc=3884)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Abstract
- 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.
