TY - GEN
T1 - Learning multilevel dictionaries for compressed sensing using discriminative clustering
AU - Thiagarajan, Jayaraman J.
AU - Ramamurthy, Karthikeyan Natesan
AU - Spanias, Andreas
AU - Nasiopoulos, Panos
N1 - Funding Information:
Some of the data presented herein were obtained at the W.M. Keck Observatory, which is operated as a scientific partnership among the California Institute of Technology, the University of California, and NASA; the observatory was made possible by the generous financial support of the W.M. Keck Foundation. This work is based in part on data produced at the Canadian Astronomy Data Centre as part of the CFHT Legacy Survey, a collaborative project of the National Research Council of Canada and the French Centre National de la Recherche Scientifique. The work is also based on observations obtained at the Gemini Observatory, which is operated by the Association of Universities for Research in Astronomy, Inc., under a cooperative agreement with the NSF on behalf of the Gemini partnership: the NSF, the STFC (United Kingdom), the National Research Council (Canada), CONICYT (Chile), the Australian Research Council (Australia), CNPq (Brazil) and CONICET (Argentina). This research used observations from Gemini program number: GN-2005A-Q-11, GN-2005B-Q-7, GN-2006A-Q-7, GS-2005A-Q-11 and GS-2005B-Q-6, and GS-2008B-Q-56. This research has made use of the NASA/IPAC Extragalactic Database (NED), which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with NASA and of data provided by the Central Bureau for Astronomical Telegrams.
Funding Information:
We thank the referee for their thorough reading of the manuscript, which helped clarify and improve it. T.d.J. also thanks P. Nugent and M. Sullivan for helpful comments on this manuscript. Support for A.V.F.’s supernova research group at U.C. Berkeley has been provided by US NSF grant AST-1211916, the TABASGO Foundation, Gary and Cynthia Bengier (T.d.J. is a Bengier Postdoctoral Fellow), the Christopher R. Redlich Fund, and the Miller Institute for Basic Research in Science (U.C. Berkeley). The work of A.V.F. was completed in part at the Aspen Center for Physics, which is supported by NSF grant PHY-1607611; he thanks the Center for its hospitality during the neutron stars workshop in 2017 June and July. L.G. was supported in part by the NSF under grant AST-1311862. S.G.G, M.H. and G.P acknowledge support from the Ministry of Economy, Development, and Tourism’s Millennium Science Initiative through grant IC120009, awarded to The Millennium Institute of Astrophysics (MAS). This work has been supported by Japan Society for the Promotion of Science (JSPS) KAKENHI Grant JP15H02075 (M.T.), JP25800103 (M.T.), JP26400222, JP16H02168 and JP17K05382 (K.N.). K.M acknowledges the JSPS Open Partnership Bilateral Joint Research Project between Japan and Chile, the YITP workshop YITP-T-16-05 supported by the Yukawa Institute for Theoretical Physics at Kyoto University, and JSPS KAKENHI Grant 17H02864. The work of the CSP-I has been supported by the NSF under grants AST-0306969, AST-0607438 and AST-1008343.
Funding Information:
5The rlap parameter is akin to a quality parameter: the higher the rlap, the better the correlation (Blondin & Tonry 2007). 6IRAF is distributed by the National Optical Astronomy Observatory, which is operated by the Association of Universities for Research in Astronomy (AURA) under cooperative agreement with the US National Science Foundation (NSF).
Funding Information:
software were developed by the NAOJ, the Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU), the University of Tokyo, the High Energy Accelerator Research Organization (KEK), the Academia Sinica Institute for Astronomy and Astrophysics in Taiwan (ASIAA), and Princeton University. Funding was contributed by the FIRST program from Japanese Cabinet Office, the Ministry of Education, Culture, Sports, Science and Technology (MEXT), the Japan Society for the Promotion of Science (JSPS), Japan Science and Technology Agency (JST), the Toray Science Foundation, NAOJ, Kavli IPMU, KEK, ASIAA, and Princeton University. The Pan-STARRS1 Surveys (PS1) have been made possible through contributions of the Institute for Astronomy, the University of Hawaii, the Pan-STARRS Project Office, the Max-Planck Society and its participating institutes, the Max Planck Institute for Astronomy, Heidelberg and the Max Planck Institute for Extraterrestrial Physics, Garching, The Johns Hopkins University, Durham University, the University of Edinburgh, Queen’s University Belfast, the Harvard-Smithsonian Center for Astrophysics, the Las Cumbres Observatory Global Telescope Network Incorporated, the National Central University of Taiwan, the Space Telescope Science Institute, the National Aeronautics and Space Administration (NASA) under Grant No. NNX08AR22G issued through the Planetary Science Division of the NASA Science Mission Directorate, the NSF under grant AST-1238877, the University of Maryland, and Eotvos Lorand University (ELTE). This paper makes use of software developed for the Large Synoptic Survey Telescope. We thank the LSST Project for making their code available as free software at http://dm.lsst.org.
PY - 2012
Y1 - 2012
N2 - The performance of sparse recovery using compressed measurements improves when dictionaries learned from training data are used in place of predefined dictionaries. In this paper, we propose to learn incoherent multilevel dictionaries using discriminative clustering in each level. To this end, we present the discriminative K-lines clustering that iterates between identifying the cluster centers and computing the discriminant directions. A scheme for computing representations using the proposed dictionary is also developed. Simulation results for compressed sensing using standard images demonstrate that incorporating incoherence in the dictionary results in improved recovery performance. Furthermore, we implement the proposed algorithms as part of a sparse representations toolbox for the J-DSP software package.
AB - The performance of sparse recovery using compressed measurements improves when dictionaries learned from training data are used in place of predefined dictionaries. In this paper, we propose to learn incoherent multilevel dictionaries using discriminative clustering in each level. To this end, we present the discriminative K-lines clustering that iterates between identifying the cluster centers and computing the discriminant directions. A scheme for computing representations using the proposed dictionary is also developed. Simulation results for compressed sensing using standard images demonstrate that incorporating incoherence in the dictionary results in improved recovery performance. Furthermore, we implement the proposed algorithms as part of a sparse representations toolbox for the J-DSP software package.
KW - Discriminative clustering
KW - compressed sensing
KW - incoherent dictionaries
KW - sparse representations
UR - http://www.scopus.com/inward/record.url?scp=84867178186&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84867178186&partnerID=8YFLogxK
U2 - 10.1109/IIH-MSP.2012.125
DO - 10.1109/IIH-MSP.2012.125
M3 - Conference contribution
AN - SCOPUS:84867178186
SN - 9780769547121
T3 - Proceedings of the 2012 8th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2012
SP - 494
EP - 497
BT - Proceedings of the 2012 8th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2012
PB - IEEE Computer Society
T2 - 2012 8th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2012
Y2 - 18 July 2012 through 20 July 2012
ER -