@conference {8999159,
	title = {Unsupervised Learning on the Health and Retirement Study using Geometric Data Analysis},
	booktitle = {2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)},
	year = {2019},
	month = {12/2019},
	publisher = { IEEE},
	organization = { IEEE},
	address = {Boca Raton, FL, USA},
	abstract = {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.},
	keywords = {Aging, Sociology, Data analysis, dimensionality reduction, Economics, Education, hierarchical clustering, multiple correspondence analysis, Retirement, Statistics},
	issn = {null},
	doi = {10.1109/ICMLA.2019.00063},
	url = {https://ieeexplore.ieee.org/abstract/document/8999159},
	author = {R. Sanchez-Arias and R. W. Batista}
}
