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## Section: Research Program

### Multivariate decompositions

Multivariate decompositions are an important tool to model complex data such as brain activation images: for instance, one might be interested in extracting an atlas of brain regions from a given dataset, such as regions depicting similar activities during a protocol, across multiple protocols, or even in the absence of protocol (during resting-state). These data can often be factorized into spatial-temporal components, and thus can be estimated through regularized Principal Components Analysis (PCA) algorithms, which share some common steps with regularized regression.

Let $𝐗$ be a neuroimaging dataset written as an $\left({n}_{subj},{n}_{voxels}\right)$ matrix, after proper centering; the model reads

where $𝐃$ represents a set of ${n}_{comp}$ spatial maps, hence a matrix of shape $\left({n}_{comp},{n}_{voxels}\right)$, and $𝐀$ the associated subject-wise loadings. While traditional PCA and independent components analysis are limited to reconstruct components $𝐃$ within the space spanned by the column of $𝐗$, it seems desirable to add some constraints on the rows of $𝐃$, that represent spatial maps, such as sparsity, and/or smoothness, as it makes the interpretation of these maps clearer in the context of neuroimaging.

This yields the following estimation problem:

The problem is not jointly convex in all the variables but each penalization given in Eq (2 ) yields a convex problem on $𝐃$ for $𝐀$ fixed, and conversely. This readily suggests an alternate optimization scheme, where $𝐃$ and $𝐀$ are estimated in turn, until convergence to a local optimum of the criterion. As in PCA, the extracted components can be ranked according to the amount of fitted variance. Importantly, also, estimated PCA models can be interpreted as a probabilistic model of the data, assuming a high-dimensional Gaussian distribution (probabilistic PCA).