Sentence examples for dimensional data analysis from inspiring English sources

Nonlinear dimensionality reduction is a challenging problem encountered in a variety of high dimensional data analysis.

Nonlinear dimensionality reduction is a challenging problem encountered in a variety of high dimensional data analysis, including machine learning, pattern recognition, scientific visualization, and neural computation.

His research is focused on statistical and machine learning methods in precision medicine, measurement error correction, cluster randomized trials, and high dimensional data analysis.

His research interests include survival analysis and semiparametric modeling; resampling method; meta-analysis; high dimensional data analysis; and personalized medicine for disease diagnosis, prognosis and treatment.

Our current areas of focus include statistical and online learning, convex and non-convex optimization, high dimensional data analysis, combinatorial optimization and its applications in AI, statistics, and probability.

We review and outline potential avenues for progress, including new theories of high dimensional data analysis, the need to analyze complex artificial networks, and methods for analyzing entire spaces of circuit models, rather than one model at a time.

The classical principal component analysis (PCA) is the most widely used tool for high-dimensional data analysis, and it plays a fundamental role in dimensionality reduction.

Zapala, M. A. & Schork, N. J. Statistical properties of multivariate distance matrix regression for high-dimensional data analysis.

Many of these studies later found applications in high-dimensional data analysis — for example, to create feature-extraction algorithms.

His research is in the area of high-dimensional data analysis.

Strong programming skills in R and computational skills for high-dimensional data analysis.