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- W4310632910 abstract "Modern data collection in many data paradigms, including bioinformatics, often incorporates multiple traits derived from different data types (i.e. platforms). We call this data multi-block, multi-view, or multi-omics data. The emergent field of data integration develops and applies new methods for studying multi-block data and identifying how different data types relate and differ. One major frontier in contemporary data integration research is methodology that can identify partially-shared structure between sub-collections of data types. This work presents a new approach: Data Integration Via Analysis of Subspaces (DIVAS). DIVAS combines new insights in angular subspace perturbation theory with recent developments in matrix signal processing and convex-concave optimization into one algorithm for exploring partially-shared structure. Based on principal angles between subspaces, DIVAS provides built-in inference on the results of the analysis, and is effective even in high-dimension-low-sample-size (HDLSS) situations." @default.
- W4310632910 created "2022-12-13" @default.
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- W4310632910 date "2022-12-01" @default.
- W4310632910 modified "2023-10-16" @default.
- W4310632910 title "Data Integration Via Analysis of Subspaces (DIVAS)" @default.
- W4310632910 doi "https://doi.org/10.48550/arxiv.2212.00703" @default.
- W4310632910 hasPublicationYear "2022" @default.
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