2018/07/18 by Antoine Recanati, Recanati, Antoine, Thomas Kerdreux +4
Biochemistry, Genetics and Molecular Biology · Computer Science · Physics and Astronomy · #Bayesian Methods and Mixture Models #Complex Network Analysis Techniques #Data Structures and Algorithms (cs.DS) #FOS: Biological sciences #FOS: Computer and information sciences #Gene expression and cancer classification #Genomics (q-bio.GN) #cs.DS #q-bio.GN
paper · pdf · doi:10.48550/arxiv.1807.07122
arxiv created 2018/07/18 · openalex publication_date 2018/07/18 · arxiv updated 2018/07/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Spectral clustering uses a graph Laplacian spectral embedding to enhance the cluster structure of some data sets. When the embedding is one dimensional, it can be used to sort the items (spectral ordering). A number of empirical results also suggests that a multidimensional Laplacian embedding enhances the latent ordering of the data, if any. This also extends to circular orderings, a case where unidimensional embeddings fail. We tackle the task of retrieving linear and circular orderings in a unifying framework, and show how a latent ordering on the data translates into a filamentary structure on the Laplacian embedding. We propose a method to recover it, illustrated with numerical experiments on synthetic data and real DNA sequencing data. The code and experiments are available at https://github.com/antrec/mdso.