DySIB recovers a two-dimensional representation matching the phase space of a physical pendulum from high-dimensional video data by maximizing predictive mutual information in latent space.
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Joint and cross covariance matrices detect shared signals earlier than self-covariances in undersampled high-dimensional data, with the better choice depending on dimensionality mismatch between the variables.
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Information bottleneck for learning the phase space of dynamics from high-dimensional experimental data
DySIB recovers a two-dimensional representation matching the phase space of a physical pendulum from high-dimensional video data by maximizing predictive mutual information in latent space.
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Better Together: Cross and Joint Covariances Enhance Signal Detectability in Undersampled Data
Joint and cross covariance matrices detect shared signals earlier than self-covariances in undersampled high-dimensional data, with the better choice depending on dimensionality mismatch between the variables.