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Found 3 papers, 1 papers with code
Discovering group dynamics in synchronous time series via hierarchical recurrent switching-state models
Our hierarchical switching recurrent dynamical models can be learned via closed-form variational coordinate ascent updates to all latent chains that scale linearly in the number of individual time series.
Projecting "better than randomly": How to reduce the dimensionality of very large datasets in a way that outperforms random projections
However, the recent development of randomized principal component analysis (RPCA) has opened up the possibility of obtaining approximate principal components on very large datasets.
Lazy stochastic principal component analysis
Stochastic principal component analysis (SPCA) has become a popular dimensionality reduction strategy for large, high-dimensional datasets.
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