IRIS: time-structured manifold projections
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Computer Science > Machine Learning
Title:IRIS: time-structured manifold projections
Abstract:High-dimensional biomedical data, such as cell-by-gene matrices, are increasingly generated temporally. However, Manifold Learning algorithms, like t-SNE and UMAP, cannot incorporate time-ordering in their layouts, obfuscating the dynamics of cell types or other classes. As a solution, we present IRIS, a new Manifold Learning algorithm that structures layouts both chronologically and by manifold topology. IRIS can visualize a wide range of dynamic biomedical data, including scRNA-seq, comparative metagenomics, and literature.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.30810 [cs.LG] |
| (or arXiv:2605.30810v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.30810
arXiv-issued DOI via DataCite (pending registration)
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