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Found 6 papers, 4 papers with code
Domain Adaptation Principal Component Analysis: base linear method for learning with out-of-distribution data
Domain adaptation is a popular paradigm in modern machine learning which aims at tackling the problem of divergence (or shift) between the labeled training and validation datasets (source domain) and a potentially large unlabeled dataset (target domain).
Quasi-orthogonality and intrinsic dimensions as measures of learning and generalisation
Recent work by Mellor et al (2021) showed that there may exist correlations between the accuracies of trained networks and the values of some easily computable measures defined on randomly initialised networks which may enable to search tens of thousands of neural architectures without training.
Scikit-dimension: a Python package for intrinsic dimension estimation
Dealing with uncertainty in applications of machine learning to real-life data critically depends on the knowledge of intrinsic dimensionality (ID).
Trajectories, bifurcations and pseudotime in large clinical datasets: applications to myocardial infarction and diabetes data
Large observational clinical datasets become increasingly available for mining associations between various disease traits and administered therapy.
Local intrinsic dimensionality estimators based on concentration of measure
In this paper, we introduce new local estimators of ID based on linear separability of multi-dimensional data point clouds, which is one of the manifestations of concentration of measure.
Estimating the effective dimension of large biological datasets using Fisher separability analysis
Modern large-scale datasets are frequently said to be high-dimensional.
