• Bogdan Dumitrescu
  • Paul Irofti


Classification is an application where sparse representation intuitively may have a great contribution, due to its ability to grasp the essentials of a signal. We present two large categories of methods. In the first, a dictionary is learned from the training signals defining each class. Classification is based on the residuals of the representations on each individual dictionary or, more often, on their union. An important DL goal modification is to encourage not only each dictionary to well represent the signals from its own class, but also to badly represent the signals from other classes, thus gaining in discriminative power. The second category is that where a single dictionary is used for representation and a classifier matrix is also trained to extract class information from the sparse representation matrix. The two most prominent methods are discriminative DL and label consistent DL. Other methods that fall somewhat in between the categories are also presented. We apply the methods to fault detection in a water network where a small number of sensors can be used to determine the location of faults.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Bogdan Dumitrescu
    • 1
  • Paul Irofti
    • 2
  1. 1.Department of Automatic Control and Systems Engineering, Faculty of Automatic Control and ComputersUniversity Politehnica of BucharestBucharestRomania
  2. 2.Department of Computer Science, Faculty of Mathematics and Computer ScienceUniversity of BucharestBucharestRomania

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