A spatially-localized penalized matrix decomposition (PMD) designed to separate (low-dimensional) signal from (temporally-uncorrelated) noise for a wide variety of functional imaging data types (including one-photon, two-photon, three-photon, widefield, somatic, axonal, dendritic, calcium, and voltage imaging). The decomposition is applied in parallel to local spatial patches of data, making the decomposition highly scalable for large datasets. Additionally, all internal tuning parameters are estimated directly from the data -- allowing for straightforward automation of pipelines including this method. The spatially-sparse, low-rank form of the decomposition -- which is significantly compressed -- facilitates the application of downstream analyses with implementations that operate directly on the factored data (e.g. LocaNMF).
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