Unsupervised Learning on the Health and Retirement Study using Geometric Data Analysis

A geometric data analysis that builds a lower dimensional representation of both individuals and measured variables is used to detect and represent underlying structures in the US Health and Retirement Study, a longitudinal survey of a representative sample of Americans over age 50 that captures information on how changing health interacts with social, economic, and psychological factors and retirement decisions. Multiple correspondence analysis is performed on a subset of the survey responses, creating a lower dimensional representation of the respondents and their response patterns, and a hierarchical clustering method is applied to test and validate specific structures in this population study.