This study employs a linear and nonlinear dimension reduction technique that expresses the probability distribution of observations based on the similarity or dissimilarity in their embedded space ...

Statisticians have introduced a new technique that accurately describes high-dimensional data using lower-dimensional smooth structures. This innovation marks a significant step forward in addressing ...

Background: Linear dimensionality reduction techniques are widely used in many applications. The goal of dimensionality reduction is to eliminate the noise of data and extract the main features of ...

In this work, we perform an extensive statistical evaluation for learning and recognition of object manipulation actions. We concentrate on single arm/hand actions but study the problem of modeling ...

This project thus promotes the advancement of science, welfare and prosperity, as stated by NSF's mission.This multidisciplinary research project aims at developing scalable end-to-end non-linear ...

Data scientists use dimensionality reduction in machine learning models to remove irrelevant features from busy datasets.