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Found 6 papers, 1 papers with code
Robust and Fast Measure of Information via Low-rank Representation
The matrix-based R\'enyi's entropy allows us to directly quantify information measures from given data, without explicit estimation of the underlying probability distribution.
Optimal Randomized Approximations for Matrix based Renyi's Entropy
The Matrix-based Renyi's entropy enables us to directly measure information quantities from given data without the costly probability density estimation of underlying distributions, thus has been widely adopted in numerous statistical learning and inference tasks.
Computationally Efficient Approximations for Matrix-based Renyi's Entropy
The recently developed matrix based Renyi's entropy enables measurement of information in data simply using the eigenspectrum of symmetric positive semi definite (PSD) matrices in reproducing kernel Hilbert space, without estimation of the underlying data distribution.
Markov subsampling based Huber Criterion
Subsampling is an important technique to tackle the computational challenges brought by big data.
Regularized Modal Regression on Markov-dependent Observations: A Theoretical Assessment
Our results show that the Markov dependence impacts on the generalization error in the way that sample size would be discounted by a multiplicative factor depending on the spectral gap of underlying Markov chain.
OpenHI2 -- Open source histopathological image platform
Transition from conventional to digital pathology requires a new category of biomedical informatic infrastructure which could facilitate delicate pathological routine.
