Abstract
Spectral clustering consists in creating, from the spectral elements of a Gaussian affinity matrix, a low-dimensional space in which data are grouped into clusters. However, questions about the separability of clusters in the projection space and the choice of the Gaussian parameter remain open. By drawing back to some continuous formulation, we propose an interpretation of spectral clustering with Partial Differential Equations tools which provides clustering properties and defines bounds for the affinity parameter.
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Mouysset, S., Noailles, J., Ruiz, D., Tauber, C. (2014). Spectral Clustering: Interpretation and Gaussian Parameter. In: Spiliopoulou, M., Schmidt-Thieme, L., Janning, R. (eds) Data Analysis, Machine Learning and Knowledge Discovery. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-01595-8_17
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DOI: https://doi.org/10.1007/978-3-319-01595-8_17
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