Title

Dimension Reduction for Big Data

Presenter Information/ Coauthors Information

Hossein Moradi, South Dakota State University

Presentation Type

Event

Track

Methodology

Abstract

In many research areas, such as health science, environmental sciences, agricultural sciences, etc., it is common to observe data with huge volume. These data could be correlated through space and/or time. Studying the relationship for such complex data calls for a fairly advanced modeling techniques. Reducing the dimensionality of the data in both covariates and response space can help researcher to better handle the computational cost.

Start Date

5-2-2019 1:00 PM

End Date

5-2-2019 1:50 PM

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Feb 5th, 1:00 PM Feb 5th, 1:50 PM

Dimension Reduction for Big Data

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In many research areas, such as health science, environmental sciences, agricultural sciences, etc., it is common to observe data with huge volume. These data could be correlated through space and/or time. Studying the relationship for such complex data calls for a fairly advanced modeling techniques. Reducing the dimensionality of the data in both covariates and response space can help researcher to better handle the computational cost.