Multi-Sensor Fusion via Reduction of Dimensionality [article]

Alon Schclar
2012 arXiv   pre-print
Large high-dimensional datasets are becoming more and more popular in an increasing number of research areas. Processing the high dimensional data incurs a high computational cost and is inherently inefficient since many of the values that describe a data object are redundant due to noise and inner correlations. Consequently, the dimensionality, i.e. the number of values that are used to describe a data object, needs to be reduced prior to any other processing of the data. The dimensionality
more » ... uction removes, in most cases, noise from the data and reduces substantially the computational cost of algorithms that are applied to the data. In this thesis, a novel coherent integrated methodology is introduced (theory, algorithm and applications) to reduce the dimensionality of high-dimensional datasets. The method constructs a diffusion process among the data coordinates via a random walk. The dimensionality reduction is obtained based on the eigen-decomposition of the Markov matrix that is associated with the random walk. The proposed method is utilized for: (a) segmentation and detection of anomalies in hyper-spectral images; (b) segmentation of multi-contrast MRI images; and (c) segmentation of video sequences. We also present algorithms for: (a) the characterization of materials using their spectral signatures to enable their identification; (b) detection of vehicles according to their acoustic signatures; and (c) classification of vascular vessels recordings to detect hyper-tension and cardio-vascular diseases. The proposed methodology and algorithms produce excellent results that successfully compete with current state-of-the-art algorithms.
arXiv:1211.2863v1 fatcat:rtqwctr4ynghpd2jblcvfvgpuq