Neural network architectures for disentangling the multimodal structure of data ensembles
We introduce neural network architectures that model the mechanism that generates data and address the difficult problem of disentangling the multimodal structure of data ensembles. We provide (i) an autoencoder-decoder architecture that implements the $M$-mode SVD and (ii) a generalized autoencoder that employs a kernel activation and implements the doubly nonlinear Kernel-MPCA. The neural network projection architecture decomposes an unlabeled data given an estimated forward model and a set of observations that constrain the solution set.
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