We present a novel parametric message representation for belief propagation (BP) that provides a novel grid-based way to address the cooperative localization problem in wireless networks. The proposed Grid-BP approach allows faster calculations than non-parametric representations and works well with existing grid-based coordinate systems, e.g., NATO’s military grid reference system (MGRS). This...

We formulate the weighted b-matching objective function as a probability distribution function and prove that belief propagation (BP) on its graphical model converges to the optimum. Standard BP on our graphical model cannot be computed in polynomial time, but we introduce an algebraic method to circumvent the combinatorial message updates. Empirically, the resulting algorithm is on average fas...

The stereo matching algorithm based on the belief propagation (BP) has the low matching error as the global method, but has the disadvantage of a long processing time. In addition to a low error of less than 2.6% in the Middlebury image simulation, a new architecture based on BP shows a high-speed parallel VLSI structure of the time complexity O(N), at properly small iterations, so that it can ...

In the context of solving large distributed constraint optimization problems (DCOP), belief-propagation and approximate inference algorithms are candidates of choice. However, in general, when the factor graph is very loopy (i.e. cyclic), these solution methods suffer from bad performance, due to non-convergence and many exchanged messages. As to improve performances of the Max-Sum inference al...

Compressive sensing (CS) is a sampling technique designed for reducing the complexity of sparse data acquisition. One of the major obstacles for practical deployment of CS techniques is the signal reconstruction time and the high storage cost of random sensing matrices. We propose a new structured compressive sensing scheme, based on codes of graphs, that allows for a joint design of structured...

Quantum systems are the future candidates for computers and information processing devices. Information about quantum states and processes may be incomplete and scattered in these systems. We use a quantum version of Belief Propagation(BP) Algorithm to integrate the distributed information. In this algorithm the distributed information, which is in the form of density matrix, can be approximate...

Significant progress in clustering has been achieved by algorithms that are based on pairwise affinities between the datapoints. In particular, spectral clustering methods have the advantage of being able to divide arbitrarily shaped clusters and are based on efficient eigenvector calculations. However, spectral methods lack a straightforward probabilistic interpretation which makes it difficul...

Fast convergence speed is a desired property for training latent Dirichlet allocation (LDA), especially in online and parallel topic modeling for massive data sets. This paper presents a novel residual belief propagation (RBP) algorithm to accelerate the convergence speed for training LDA. The proposed RBP uses an informed scheduling scheme for asynchronous message passing, which passes fast-co...

In this paper, the decoding of lowdensity parity-check (LDPC) codes is considered. A new algorithm, named λ−Min algorithm, for updating extrinsic information is proposed. The λ−Min algorithm offers different trade-off performance versus complexity between the belief propagation (BP) algorithm (optimal but complex) and the universal most powerful (UMP) BP-based algorithm (simple but leading to s...

This paper investigates the problem of sparse support detection (SSD) via a detection-oriented algorithm named Bayesian hypothesis test via belief propagation (BHT-BP) [7],[8]. Our main focus is to compare BHT-BP to an estimation-based algorithm, called CS-BP [3], and show its superiority in the SSD problem. For this investigation, we perform a phase transition (PT) analysis over the plain of t...