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We consider the problem of recovering a smooth graph signal from noisy samples taken at a small number of graph nodes. The recovery problem is formulated as a convex optimization problem which minimizes the total variation (accounting for the smoothness of the graph signal) while controlling the empirical error. We solve this total variation minimization problem efficiently by applying a recent algorithm...
Factorization of a single matrix or tensor has been used widely to reveal interpretable factors or predict missing data. However, in many cases side information may be available, such as social network activities and user demographic data together with Netflix data. In these situations, coupled matrix tensor factorization (CMTF) can be employed to account for additional sources of information. When...
We consider the problem of maximizing the total wireless signal power delivered by a distributed antenna array to a receiver where the transmitting nodes each have known frequency-selective channel responses to the receiver and are subject to individual total transmit power constraints. This optimization problem is mathematically quite different from the power maximization problems involving single...
To enable low-rank tensor completion and factorization, this paper puts forth a novel tensor rank regularization method based on the ℓ1,2-norm of the tensor's parallel factor analysis (PARAFAC) factors. Specifically, for an N-way tensor, upon collecting the magnitudes of its rank-1 components in a vector, the proposed regularizer controls the tensor's rank by inducing sparsity in the vector of magnitudes...
In this paper, we discuss how to design the graph topology to reduce the communication complexity of certain algorithms for decentralized optimization. Our goal is to minimize the total communication needed to achieve a prescribed accuracy. We discover that the so-called expander graphs are near-optimal choices. We propose three approaches to construct expander graphs for different numbers of nodes...
We give an overview of recent developments in numerical optimization-based computation of tensor decompositions that have led to the release of Tensorlab 3.0 in March 2016 (www.tensorlab.net). By careful exploitation of tensor product structure in methods such as quasi-Newton and nonlinear least squares, good convergence is combined with fast computation. A modular approach extends the computation...
This paper is addressed to the problem of identifying the neighborhood structure of an undirected graph, whose nodes are labeled with the elements of a multivariate normal (MVN) random vector. A semi-definite program is given for estimating the information matrix under arbitrary constraints on its elements. More importantly, closed-form expressions are given for the maximum likelihood (ML) estimator...
In this paper, the estimation of a narrowband time-varying channel under the practical assumptions of finite block length and finite transmission bandwidth is investigated. It is shown that the signal, after passing through a time-varying narrowband channel reveals a particular parametric low-rank structure that can be represented as a bilinear form. To estimate the channel, we propose two structured...
We provide an optimization of the performance of joint transmit precoding and transmit antenna selection for spatial modulation. We show that the joint optimization problem out-performs the separate optimization problem by up to 1 dB. The performance of different optimization schemes with varying computational complexity is simulated and we discuss their individual advantages.
Consensus-based algorithms are a welcome approach to establish cooperative communication systems. In this work, it has been utilized to present a hardware architecture for distributed data detection in a receiver with several sensor nodes on an easily scalable regular network. Additionally, it is capable to detect the transmitted message even if a single sensor node fails to operate. On top of that,...
Learn 2D filter banks are currently being used in high-impact applications such convolutional neural networks, convolutional sparse representations, etc. However such filter banks usually have plentiful filters, each being non-separable, accounting for a large portion of the overall computational cost. In this paper we propose a novel and computationally appealing alternating optimization based algorithm...
Application performance on these processor array platforms is highly sensitive to how functionality is physically placed on the device, as this choice crucially determines communication latencies and congestion patterns of the on-chip inter-core communication. The problem of identifying the best, or just a good enough, partitioning and placement does not, in general, admit to an analytic solution,...
Subspace clustering has become a popular tool for clustering high-dimensional non-linearly separable data. However, state-of-the-art subspace clustering algorithms do not scale well as the number of data increases. The present paper puts forth a novel randomized subspace clustering scheme for high-volume data based on random projections. Performance of the proposed method is assessed via numerical...
High-resolution phase retrieval is challenging due to the low signal-to-noise ratio of measurements. This work utilizes variable splitting and alternating minimization to simultaneously enforce a 1-norm data fit penalty, an analysis-form sparse regularizer, and nonnegativity or real-valued image constraints to resolve an image from squared-magnitude measurements. The reconstruction algorithm incorporates...
The growing complexity of digital signal processing applications make a compelling case the use of high-level design and synthesis methodologies for the implementation on reconfigurable and embedded devices. Past research has shown that raising the level of abstraction of design stages does not necessarily gives penalties in terms of performance or resources. Dataflow programs provide behavioral descriptions...
Sigmoid and Hyperbolic Tangent are widely used as activation functions in artificial neural networks. Exponential term and division are basic building blocks of these functions. This paper proposes precise and efficient hardware implementations for sigmoid and hyperbolic tangent functions using exponential function approximation. Performance of both functions has been verified which shows that the...
In this work, we consider distributed adaptive learning over multitask mean-square-error (MSE) networks where each agent is interested in estimating its own parameter vector, also called task, and where the tasks at neighboring agents are related according to a set of linear equality constraints. We assume that each agent knows its own cost function of its vector and the set of constraints involving...
The paper studies distributed Dictionary Learning (DL) problems where the learning task is distributed over a multi-agent network with time-varying (nonsymmetric) connectivity. This formulation is relevant, for instance, in Big Data scenarios where massive amounts of data are collected/stored in different spatial locations and it is unfeasible to aggregate and/or process all data in a fusion center,...
We propose an asynchronous, decentralized algorithm for consensus optimization. The algorithm runs over a network of agents, where the agents perform local computation and communicate with neighbors. We design the algorithm so that the agents can compute and communicate independently at different times and for different durations. This reduces the waiting time for the slowest agent or longest communication...
Temperature/Emissivity separation (TES) techniques are desirable in hyperspectral image processing to extract the emissivity spectra and temperature of the materials in the image. We review a model for the hyperspectral image pixels that we have been developing, for which a maximum likelihood can be used for tem-perature/emissivity separation. Optimization of the ridge-like log likelihood function...
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