Intelligent Data Fusion Using Sparse Representations and Nonlinear Dimensionality Reduction

摘要

We propose a new method for performing data fusion and subsequent classification in an information-efficient manner. We argue that an algorithm that can find sparse, low-dimensional representations of data is an excellent candidate for data fusion and classification. Two recent developments in signal processing are investigated: 1) The use of over-determined dictionaries (e.g., frames), and 2) the use of so-called nonlinear dimensionality reduction techniques.