Results 1  10
of
497
Proximal thresholding algorithm for minimization over orthonormal bases
 SIAM Journal on Optimization
, 2007
"... The notion of soft thresholding plays a central role in problems from various areas of applied mathematics, in which the ideal solution is known to possess a sparse decomposition in some orthonormal basis. Using convexanalytical tools, we extend this notion to that of proximal thresholding and inve ..."
Abstract

Cited by 62 (14 self)
 Add to MetaCart
The notion of soft thresholding plays a central role in problems from various areas of applied mathematics, in which the ideal solution is known to possess a sparse decomposition in some orthonormal basis. Using convexanalytical tools, we extend this notion to that of proximal thresholding
Outline Optimization Soft thresholding Iterative thresholding Proximal thresholding Applications
, 2007
"... Optimization in image processing Ideal image x described by pieces of informations (or just beliefs..) (Ψi)i∈I in some real Hilbert space H. Set theoretic/feasible solution approach: find x ∈ i∈I ..."
Abstract
 Add to MetaCart
Optimization in image processing Ideal image x described by pieces of informations (or just beliefs..) (Ψi)i∈I in some real Hilbert space H. Set theoretic/feasible solution approach: find x ∈ i∈I
Loopy belief propagation for approximate inference: An empirical study. In:
 Proceedings of Uncertainty in AI,
, 1999
"... Abstract Recently, researchers have demonstrated that "loopy belief propagation" the use of Pearl's polytree algorithm in a Bayesian network with loops can perform well in the context of errorcorrecting codes. The most dramatic instance of this is the near Shannonlimit performanc ..."
Abstract

Cited by 676 (15 self)
 Add to MetaCart
limit performance of "Turbo Codes" codes whose decoding algorithm is equivalent to loopy belief propagation in a chainstructured Bayesian network. In this paper we ask: is there something spe cial about the errorcorrecting code context, or does loopy propagation work as an ap proximate inference scheme
SIAM J. Optim. to appear PROXIMAL THRESHOLDING ALGORITHM FOR MINIMIZATION OVER ORTHONORMAL BASES ∗
"... Abstract. The notion of soft thresholding plays a central role in problems from various areas of applied mathematics, in which the ideal solution is known to possess a sparse decomposition in some orthonormal basis. Using convexanalytical tools, we extend this notion to that of proximal thresholdin ..."
Abstract
 Add to MetaCart
Abstract. The notion of soft thresholding plays a central role in problems from various areas of applied mathematics, in which the ideal solution is known to possess a sparse decomposition in some orthonormal basis. Using convexanalytical tools, we extend this notion to that of proximal
IMAGE DECONVOLUTION UNDER POISSON NOISE USING SPARSE REPRESENTATIONS AND PROXIMAL THRESHOLDING ITERATION
, 802
"... We propose an image deconvolution algorithm when the data is contaminated by Poisson noise. The image to restore is assumed to be sparsely represented in a dictionary of waveforms such as the wavelet or curvelet transform. Our key innovations are: First, we handle the Poisson noise properly by using ..."
Abstract
 Add to MetaCart
We propose an image deconvolution algorithm when the data is contaminated by Poisson noise. The image to restore is assumed to be sparsely represented in a dictionary of waveforms such as the wavelet or curvelet transform. Our key innovations are: First, we handle the Poisson noise properly by using the Anscombe variance stabilizing transform leading to a nonlinear degradation equation with additive Gaussian noise. Second, the deconvolution problem is formulated as the minimization of a convex functional with a datafidelity term reflecting the noise properties, and a nonsmooth sparsitypromoting penalties over the image representation coefficients (e.g. ℓ1norm). Third, a fast iterative backwardforward splitting algorithm is proposed to solve the minimization problem. We derive existence and uniqueness conditions of the solution, and establish convergence of the iterative algorithm. Experimental results are carried out to show the striking benefits gained from taking into account the Poisson statistics of the noise. These results also suggest that using sparsedomain regularization may be tractable in many deconvolution applications, e.g. astronomy or microscopy.
Classification on Pairwise Proximity Data
, 1999
"... We investigate the problem of learning a classification task on data represented in terms of their pairwise proximities. This representation does not refer to an explicit feature representation of the data items and is thus more general than the standard approach of using Euclidean feature vectors, ..."
Abstract

Cited by 62 (8 self)
 Add to MetaCart
threshold model in the proximity values themselves, which is optimized using Structural Risk Minimization. We show that prior knowledge about the problem can be incorporated by the choice of distance measures and examine different metrics w.r.t. their generalization. Finally, the algorithms are successfully
TAGs: Scalable ThresholdBased Algorithms for Proximity Computation in Graphs
"... A fundamental and very useful operation in graphs is the computation of the proximity between nodes, i.e., the degree of dissimilarity (or similarity) between two nodes v and u. This is an important tool both in graph databases and graph mining applications, because it provides the base to support m ..."
Abstract
 Add to MetaCart
A fundamental and very useful operation in graphs is the computation of the proximity between nodes, i.e., the degree of dissimilarity (or similarity) between two nodes v and u. This is an important tool both in graph databases and graph mining applications, because it provides the base to support
Dendritic lowthreshold calcium currents in thalamic relay cells
 J. Neurosci
, 1997
"... The lowthreshold calcium current (I T) underlies burst generation in thalamocortical (TC) relay cells and plays a central role in the genesis of synchronized oscillations by thalamic circuits. Here we have combined in vitro recordings and computational modeling techniques to investigate the consequ ..."
Abstract

Cited by 74 (11 self)
 Add to MetaCart
The lowthreshold calcium current (I T) underlies burst generation in thalamocortical (TC) relay cells and plays a central role in the genesis of synchronized oscillations by thalamic circuits. Here we have combined in vitro recordings and computational modeling techniques to investigate
Privacyaware proximity based services
 In MDM
, 2009
"... Proximity based services are location based services (LBS) in which the service adaptation depends on the comparison between a given threshold value and the distance between a user and other (possibly moving) entities. While privacy preservation in LBS has lately received much attention, very limite ..."
Abstract

Cited by 26 (1 self)
 Add to MetaCart
Proximity based services are location based services (LBS) in which the service adaptation depends on the comparison between a given threshold value and the distance between a user and other (possibly moving) entities. While privacy preservation in LBS has lately received much attention, very
Classification on Pairwise Proximity Data
, 1999
"... We investigate the problem of learning a classification task on data represented in terms of their pairwise proximities. This representation does not refer to an explicit feature representation of the data items and is thus more general than the standard approach of using Euclidean feature vectors, ..."
Abstract
 Add to MetaCart
threshold model in the proximity values themselves, which is optimized using Structural Risk Minimization. We show that prior knowledge about the problem can be incorporated by the choice of distance measures and examine different metrics w.r.t. their generalization. Finally, the algorithms are successfully
Results 1  10
of
497