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In the realm of data analysis and machine learning, the abundance of information often comes hand in hand with the curse of high dimensionality. As datasets grow larger and more complex, the challenge ...

Many areas of science depend on exploratory data analysis and visualization. The need to analyze large amounts of multivariate data raises the fundamental problem of dimensionality reduction: how to ...

We propose a dimension-reduction method based on the aggregation of localized estimators. The dual process of localization and aggregation helps to mitigate the bias due to the symmetry in the ...

“Single-cell omics” is producing exciting biological data. The field measures how active every gene is in every cell of an organism and promises to yield valuable information on how our bodies work.

A classification problem is a supervised learning problem that asks for a choice between two or more classes, usually providing probabilities for each class. Leaving out neural networks and deep ...

Advances in computer science are helping to accelerate a broad spectrum of scientific research. The more complex the problem, the greater the potential for artificial intelligence (AI) machine ...