A limited comparative study of dimension reduction techniques on CAESAR

Understanding and organizing data is the first step toward exploiting sensor phenomenology. What features are good for distinguishing people and what measurements, or combination of measurements, can be used to classify people by demographic characteristics including gender? Dimension reduction techniques such as Diffusion Maps that intuitively make sense [1] and Principal Component Analysis (PCA) have demonstrated the potential to aid in extracting such features. This paper briefly describes the Diffusion Map technique and PCA. More importantly, it compares two different classifiers, K-Nearest Neighbors (KNN) and Adaptive boost (Adaboost), for gender classification using these two dimension reduction techniques. The results are compared on the Civilian American and European Surface Anthropometry Resource Project (CAESAR) database, provided by the Air Force Research Laboratory (AFRL) Human Effectiveness Directorate and SAE International. We also compare the results described herein with those of other classification work performed on the same dataset, for completeness.