Feature reduction through manifold learning for a geospatial model of ambient soundscapes

Manifold learning is a type of nonlinear dimensionality reduction that helps identify a minimal combination of features to characterize data. This presentation discusses the use of diffusion maps, a type of manifold learning, on a dataset of 68 geospatial features which cover the contiguous United States. These geospatial features have been used previously to make predictions of ambient soundscapes through an ensemble of machine learning models. As the current model capabilities are expanded to predict the ambient soundscape outside of the contiguous United States, decisions must be made about which geospatial features are required for accurate models. In particular, some of these 68 features are not available or are expensive to obtain for regions outside of the contiguous United States. Diffusion maps can assist in identifying the features, or combinations of features, that best characterize the data space. [Work supported by a U.S. Army SBIR.]Manifold learning is a type of nonlinear dimensionality reduction that helps identify a minimal combination of features to characterize data. This presentation discusses the use of diffusion maps, a type of manifold learning, on a dataset of 68 geospatial features which cover the contiguous United States. These geospatial features have been used previously to make predictions of ambient soundscapes through an ensemble of machine learning models. As the current model capabilities are expanded to predict the ambient soundscape outside of the contiguous United States, decisions must be made about which geospatial features are required for accurate models. In particular, some of these 68 features are not available or are expensive to obtain for regions outside of the contiguous United States. Diffusion maps can assist in identifying the features, or combinations of features, that best characterize the data space. [Work supported by a U.S. Army SBIR.]