Technical Program

NSSIMa.PD: New Sensing and Statistical Inference Methods I

Symposium: New Sensing and Statistical Inference Methods
Session Type: Poster
Time: Tuesday, December 3, 13:30 - 15:30
Location: Poster Area D
 
NSSIMa.PD.1: PLUG-AND-PLAY PRIORS FOR MODEL BASED RECONSTRUCTION
         Singanallur Venkatakrishnan; Purdue University
         Charles A. Bouman; Purdue University
         Brendt Wohlberg; Los Alamos National Laboratory
 
NSSIMa.PD.2: SINGLE IMAGE SUPER RESOLUTION VIA MANIFOLD LINEAR APPROXIMATION USING SPARSE SUBSPACE CLUSTERING
         Chinh Dang; Michigan State University
         Mohammad Aghagolzadeh; Michigan State University
         Abdolreza Moghadam; Michigan State University
         Hayder Radha; Michigan State University
 
NSSIMa.PD.3: SPATIO-SPECTRAL ANOMALOUS CHANGE DETECTION IN HYPERSPECTRAL IMAGERY
         James Theiler; Los Alamos National Laboratory
 
NSSIMa.PD.4: LOCAL ERROR DETECTION IN SPARSE MAGNETIC RESONANCE IMAGING
         Vimal Singh; The University of Texas at Austin
         Ahmed H. Tewfik; The University of Texas at Austin
 
NSSIMa.PD.5: FUNDAMENTAL LIMITS FOR SUPPORT RECOVERY OF TREE-SPARSE SIGNALS FROM NOISY COMPRESSIVE SAMPLES
         Akshay Soni; University of Minnesota, Twin Cities
         Jarvis Haupt; University of Minnesota, Twin Cities
 
NSSIMa.PD.6: ROBUST AND SPARSE ESTIMATION OF TENSOR DECOMPOSITIONS
         Hyon-Jung Kim; Aalto University
         Esa Ollila; Aalto University
         Visa Koivunen; Aalto University
         Christophe Croux; K.U. Leuven
 
NSSIMa.PD.7: MULTISCALE SPARSE REPRESENTATION CLASSIFICATION FOR ROBUST HYPERSPECTRAL IMAGE ANALYSIS
         Minshan Cui; University of Houston
         Saurabh Prasad; University of Houston
 
NSSIMa.PD.8: NEARLY OPTIMAL LINEAR EMBEDDINGS INTO VERY LOW DIMENSIONS
         Elyot Grant; Massachusetts Institute of Technology
         Chinmay Hegde; Massachusetts Institute of Technology
         Piotr Indyk; Massachusetts Institute of Technology
 
NSSIMa.PD.9: LEARNING OVERCOMPLETE DICTIONARIES WITH L0-SPARSE NON-NEGATIVE MATRIX FACTORISATION
         Ken O'Hanlon; Queen Mary, University of London
         Mark D. Plumbley; Queen Mary, University of London
 
NSSIMa.PD.10: LEARNING FEATURES IN DEEP LEARNING ARCHITECTURES WITH UNSUPERVISED KERNEL K-MEANS
         Karl Ni; Lawrence Livermore National Laboratory
         Ryan Prenger; Lawrence Livermore National Laboratory
 
NSSIMa.PD.11: COMPRESSIVE ANOMALY DETECTION IN LARGE NETWORKS
         Xiao Li; University of California, Davis
         H. Vincent Poor; Princeton University
         Anna Scaglione; University of California, Davis
 
NSSIMa.PD.12: BAYESIAN PAIRWISE COLLABORATION DETECTION IN EDUCATIONAL DATASETS
         Andrew Waters; Rice University
         Christoph Studer; Rice University
         Richard Baraniuk; Rice University
 
NSSIMa.PD.13: CLUSTERING ON MULTI-LAYER GRAPHS VIA SUBSPACE ANALYSIS ON GRASSMANN MANIFOLDS
         Xiaowen Dong; École Polytechnique Fédérale de Lausanne
         Pascal Frossard; École Polytechnique Fédérale de Lausanne
         Pierre Vandergheynst; École Polytechnique Fédérale de Lausanne
         Nikolai Nefedov; Eidgenössische Technische Hochschule Zürich
 
NSSIMa.PD.14: THRESHOLD EFFECTS IN PARAMETER ESTIMATION FROM COMPRESSED DATA
         Pooria Pakrooh; Colorado State University
         Ali Pezeshki; Colorado State University
         Louis L. Scharf; Colorado State University
 
NSSIMa.PD.15: SPARSE REPRESENTATION CLASSIFICATION VIA SEQUENTIAL LASSO SCREENING
         Yun Wang; Princeton University
         Xu Chen; Princeton University
         Peter J. Ramadge; Princeton University
 
NSSIMa.PD.16: A THEORETICAL FRAMEWORK FOR SURROGATE SUPERVISION MULTIVIEW LEARNING
         Raviv Raich; Oregon State University