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- W4308828366 abstract "Selecting subsets of features that differentiate between two conditions is a key task in a broad range of scientific domains. In many applications, the features of interest form clusters with similar effects on the data at hand. To recover such clusters we develop DiSC, a data-driven approach for detecting groups of features that differentiate between conditions. For each condition, we construct a graph whose nodes correspond to the features and whose weights are functions of the similarity between them for that condition. We then apply a spectral approach to compute subsets of nodes whose connectivity differs significantly between the condition-specific feature graphs. On the theoretical front, we analyze our approach with a toy example based on the stochastic block model. We evaluate DiSC on a variety of datasets, including MNIST, hyperspectral imaging, simulated scRNA-seq and task fMRI, and demonstrate that DiSC uncovers features that better differentiate between conditions compared to competing methods." @default.
- W4308828366 created "2022-11-16" @default.
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- W4308828366 date "2022-11-09" @default.
- W4308828366 modified "2023-10-16" @default.
- W4308828366 title "DiSC: Differential Spectral Clustering of Features" @default.
- W4308828366 doi "https://doi.org/10.48550/arxiv.2211.05314" @default.
- W4308828366 hasPublicationYear "2022" @default.
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