HEE
This python module offers a series of Spectral Learning techniques for Hypergraph embedding.
How to use HEE
The implementation of all methods are available in the python script hee.methods.spectral_methods.py.
New spectral embedding techniques must be implemented extending the base class SpectralEmbeddingFramework and the class must implement the methods:
_laplacian(self)(private) that returns the hypergraph laplacian matrix;fit(self, dim, **kwargs)that returns the vertex embeddings matrix.
Quick look
We provide a python notebook when the implemented techniques are compared on a clustering task on the ZOO dataset.
List of supported methods
ZhouD. Zhou, J. Huang, and B. Schölkopf. 2007. Learning with Hypergraphs: Clustering, Classification, and Embedding. In Proceedings of Neural Information Processing Systems. 1601–1608RenP. Ren, R. C. Wilson, and E. R. Hancock. 2008. Spectral Embedding of Feature Hypergraphs. In Structural, Syntactic, and Statistical Pattern Recognition. 308–317BollaM. Bolla. 1993. Spectra, Euclidean representations and clusterings of hypergraphs. Discrete Math. 117 (1993), 19–39. Issue 1-ZhuY. Zhu, Z. Guan, T. Tan, H. Liu, D. Cai, and X. He. 2016. Heterogeneous hypergraph embedding for document recommendation. Neurocomputing 216 (2016), 150–162LuoF. Luo, B. Du, L. Zhang, L. Zhang, and D. Tao. 2019. Feature Learning Using Spatial-Spectral Hypergraph Discriminant Analysis for Hyperspectral Image. IEEE Trans. on Cybernetics 49, 7 (2019), 2406–2419RodriguezJ.A. Rodrìguez. 2002. On the Laplacian Eigenvalues and Metric Parameters of Hypergraphs. Linear and Multilinear Algebra 50, 1 (2002), 1–14.SaitoS. Saito, D. P. Mandic, and H. Suzuki. 2018. Hypergraph p-Laplacian: A Differential Geometry View. Proceedings of the AAAI Conference on Artificial Intelligence 32, 1 (2018)
Requirements
- HypernetX
- Numpy
- Scipy
- sklearn