Cel Parsimony, manifested as variously structured sparse and low rank representations of data, has been shown as a tremendously successful model in numerous domains of science, including signal and image processing, computer vision, and machine learning problems. Despite this success, parsimonious representation pursuit approaches practiced today face serious limitations stemming from their reliance on iterative optimization. In this project, we propose to develop a novel approach to parsimonious modeling that puts the pursuit process itself at the center, surfacing crucial aspects that are currently lost deep inside the optimization machinery. First, we will study the theoretical performance limitations of pursuit processes constrained by a fixed computational complexity budget, devising bounds on the tradeoff between performance and complexity (in the spirit of the rate-distortion tradeoff). Second, we will develop a principled way to construct families of pursuit processes that approach optimal performance at fixed complexity given a specific input data distribution, and devise tools for learning such processes on real data. Abandoning iterative representation pursuit in favour of a learned fixed-complexity function can lead to a dramatic improvement in performance, enabling previously impossible applications. It will also allow including parsimonious models into higher-level optimization problems, leading to novel modeling capabilities. In lieu of the existing generative parsimonious models, we will develop novel discriminative counterparts for uni- and multi-modal data, and show their utility in large-scale similarity learning. We will also construct efficient parsimonious modeling tools for problems involving unknown data transformation or correspondence. We will apply these methods to several challenging real-world problems in signal processing, computer vision, medical imaging, and multimedia retrieval, which will be developed to the level of prototype systems. Dziedzina nauki engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsignal processingnatural sciencescomputer and information sciencesartificial intelligencecomputer visionengineering and technologymedical engineeringdiagnostic imagingnatural sciencescomputer and information sciencesartificial intelligencemachine learning Program(-y) FP7-IDEAS-ERC - Specific programme: "Ideas" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013) Temat(-y) ERC-SG-PE6 - ERC Starting Grant - Computer science and informatics Zaproszenie do składania wniosków ERC-2013-StG Zobacz inne projekty w ramach tego zaproszenia System finansowania ERC-SG - ERC Starting Grant Instytucja przyjmująca TECHNION - ISRAEL INSTITUTE OF TECHNOLOGY Wkład UE € 764 367,55 Adres SENATE BUILDING TECHNION CITY 32000 Haifa Izrael Zobacz na mapie Rodzaj działalności Higher or Secondary Education Establishments Kontakt administracyjny Mark Davison (Mr.) Kierownik naukowy Alexander Bronstein (Dr.) Linki Kontakt z organizacją Opens in new window Strona internetowa Opens in new window Koszt całkowity Brak danych Beneficjenci (2) Sortuj alfabetycznie Sortuj według wkładu UE Rozwiń wszystko Zwiń wszystko TECHNION - ISRAEL INSTITUTE OF TECHNOLOGY Izrael Wkład UE € 764 367,55 Adres SENATE BUILDING TECHNION CITY 32000 Haifa Zobacz na mapie Rodzaj działalności Higher or Secondary Education Establishments Kontakt administracyjny Mark Davison (Mr.) Kierownik naukowy Alexander Bronstein (Dr.) Linki Kontakt z organizacją Opens in new window Strona internetowa Opens in new window Koszt całkowity Brak danych TEL AVIV UNIVERSITY Izrael Wkład UE € 704 832,45 Adres RAMAT AVIV 69978 Tel Aviv Zobacz na mapie Rodzaj działalności Higher or Secondary Education Establishments Kontakt administracyjny Lea Pais (Ms.) Linki Kontakt z organizacją Opens in new window Strona internetowa Opens in new window Koszt całkowity Brak danych