Spherical and Hyperbolic Embeddings of Data

By Richard C. Wilson, Edwin Robert Hancock, Elzbieta Pekalska, Robert P. W. Duin. 2014

135 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Nicola De Cao
2. Thomas Kipf
3. Tim R. Davidson
4. Luca Falorsi
5. Christopher R'e
6. Jure Leskovec
7. Xavier Pennec
8. J. Tomczak

In Signal Processing

1. Jure Leskovec
2. Xavier Pennec
3. J. Tomczak
4. Christopher R'e
5. Ines Chami
6. Thomas Kipf
7. Nicola De Cao
8. Rex Ying

Citations in the corpus

• A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications (2017, neutral): “The example classifiers include SVM ( [1], [20], [33], [34], [41], [42], [45], [56], [57], [60], [73], [75], [81], [87]), logistic regression ( [1], [17], [19], [20], [21], [25], [27], [28], [45], [59], [124]) and k-nearest neighbour classification ( [58], [151]).”
• Hyperbolic Graph Convolutional Neural Networks (2019, result): “Similar derivations can be found in the literature [43].”
• Barycentric subspace analysis on manifolds (2016, background): “As non-flat constant curvature spaces, both spherical and hyperbolic spaces are now considered in manifold learning for embedding data (Wilson et al., 2014).”
• Hyperspherical Variational Auto-Encoders (2018, methodology): “Specific applications of the vMF can be further found ranging from computer vision, where it is used to infer structure from motion (Guan and Smith, 2017) in spherical video, or structure from texture (Wilson et al., 2014), to natural language processing, where it is utilized in text analysis (Baner”