Information subspace-based fusion for vehicle classification

Union of Subspaces (UoS) is a new paradigm for signal modeling and processing, which is capable of identifying more complex trends in data sets than simple linear models. Relying on a bi-sparsity pursuit framework and advanced non-smooth optimization techniques, the Robust Subspace Recovery (RoSuRe) algorithm was introduced in the recent literature as a reliable and numerically efficient algorithm to unfold unions of subspaces. In this study, we apply RoSuRe to prospect the structure of a data type (e.g. sensed data on vehicle through passive audio and magnetic observations). Applying RoSuRe to the observation data set, we obtain a new representation of the time series, respecting an underlying UoS model. We subsequently employ Spectral Clustering on the new representations of the data set. The classification performance on the dataset shows a considerable improvement compared to direct application of other unsupervised clustering methods.