-
Maaten, L. van den; Hinton, G.: Visualizing non-metric similarities in multiple maps (2012)
0.04
0.04194205 = product of:
0.1677682 = sum of:
0.1677682 = weight(_text_:objects in 3884) [ClassicSimilarity], result of:
0.1677682 = score(doc=3884,freq=4.0), product of:
0.33668926 = queryWeight, product of:
5.315071 = idf(docFreq=590, maxDocs=44218)
0.06334615 = queryNorm
0.49828792 = fieldWeight in 3884, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
5.315071 = idf(docFreq=590, maxDocs=44218)
0.046875 = fieldNorm(doc=3884)
0.25 = coord(1/4)
- 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.
-
Maaten, L. van den: Accelerating t-SNE using Tree-Based Algorithms (2014)
0.03
0.03460042 = product of:
0.13840169 = sum of:
0.13840169 = weight(_text_:objects in 3886) [ClassicSimilarity], result of:
0.13840169 = score(doc=3886,freq=2.0), product of:
0.33668926 = queryWeight, product of:
5.315071 = idf(docFreq=590, maxDocs=44218)
0.06334615 = queryNorm
0.41106653 = fieldWeight in 3886, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
5.315071 = idf(docFreq=590, maxDocs=44218)
0.0546875 = fieldNorm(doc=3886)
0.25 = coord(1/4)
- Abstract
- The paper investigates the acceleration of t-SNE-an embedding technique that is commonly used for the visualization of high-dimensional data in scatter plots-using two tree-based algorithms. In particular, the paper develops variants of the Barnes-Hut algorithm and of the dual-tree algorithm that approximate the gradient used for learning t-SNE embeddings in O(N*logN). Our experiments show that the resulting algorithms substantially accelerate t-SNE, and that they make it possible to learn embeddings of data sets with millions of objects. Somewhat counterintuitively, the Barnes-Hut variant of t-SNE appears to outperform the dual-tree variant.
-
Jäger, L.: Von Big Data zu Big Brother (2018)
0.01
0.008582529 = product of:
0.034330115 = sum of:
0.034330115 = weight(_text_:22 in 5234) [ClassicSimilarity], result of:
0.034330115 = score(doc=5234,freq=2.0), product of:
0.22182742 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.06334615 = queryNorm
0.15476047 = fieldWeight in 5234, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.03125 = fieldNorm(doc=5234)
0.25 = coord(1/4)
- Date
- 22. 1.2018 11:33:49