F. Stoican and Irofti, P., “Aiding Dictionary Learning Through Multi-Parametric Sparse Representation”, Algorithms, vol. 12, p. 131-148 (18 pages), 2019.
P. Irofti and Stoican, F., “Dictionary learning strategies for sensor placement and leakage isolation in water networks”, in IFAC World Congress Conference, Toulouse, France, 2017.Abstract
This paper deals with the problem of fault detection and isolation in water networks.We consider classification strategies for sensor placement and subsequent dictionary learning and classification for accurate fault detection and isolation. Various sensor placement strategies are proposed and it is shown that faults with varying magnitudes are correctly identified in a detailed emulation benchmark.
P. Irofti, “The effect of atom replacement strategies on dictionary learning”, in INTERNATIONAL TRAVELING WORKSHOP ON INTERACTIONS BETWEEN SPARSE MODELS AND TECHNOLOGY, Aalborg, Denmark, 2016.Abstract
The sparse representations field presents a wide set of algorithms for learning overcomplete dictionaries. During the learning process many of the dictionary columns remain unused by the resulting representations. In this paper we present a few replacement strategies and their direct impact on a set of popular algorithms such as K-SVD. Experiments show significant reductions in the representation error and also evidentiate clear differences between the strategies.