Open AccessIntrinsic dimensionality of human behavioral activity data
Author(s) -
Luana Fragoso,
Tuhin Paul,
Flaviu Vadan,
Kevin G. Stanley,
Scott Bell,
Nathaniel D. Osgood
Publication year - 2019
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0218966
Subject(s) - curse of dimensionality , computer science , data science , dimension (graph theory) , intrinsic dimension , scope (computer science) , data mining , correlation , dimensionality reduction , machine learning , mathematics , geometry , pure mathematics , programming language
Patterns of spatial behavior dictate how we use our infrastructure, encounter other people, or are exposed to services and opportunities. Understanding these patterns through the analysis of data commonly available through commodity smartphones has become an important arena for innovation in both academia and industry. The resulting datasets can quickly become massive, indicating the need for concise understanding of the scope of the data collected. Some data is obviously correlated (for example GPS location and which WiFi routers are seen). Codifying the extent of these correlations could identify potential new models, provide guidance on the amount of data to collect, and even provide actionable features. However, identifying correlations, or even the extent of correlation, is difficult because the form of the correlation must be specified. Fractal-based intrinsic dimensionality directly calculates the minimum number of dimensions required to represent a dataset. We provide an intrinsic dimensionality analysis of four smartphone datasets over seven input dimensions, and empirically demonstrate an intrinsic dimension of approximately two.