Evaluation of Dimensionality Reduction Techniques: A Simulation Study (Poster 4)

• In Event: AERA Poster Session Five
  In Poster Session: Division D Section 2 Poster Session

Abstract

To better understand data, researchers often apply several techniques for data mining or data screening. In the past years, researchers have explored dimensionality reduction issues in various research areas, such as data mining, data visualization, text mining, and image classification. Since dimensionality reduction has such a wide range of applications, selecting an appropriate dimension reduction technique would be efficient to extract valuable information in the data analysis process, which could increase the model accuracy. Thus, this proposal will focus on data mining issues, which will compare and discuss several commonly used dimensionality reduction techniques with different data features.

Authors

• Yuting Chen, Syracuse University
  Presenting Author

• Haiyan Bai, University of Central Florida
  Presenting Author