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Signals evolving over graphs emerge naturally in a number of applications related to network science. A frequently encountered challenge pertains to reconstructing such signals given their values on subsets of vertices at possibly different time instants. Spatiotemporal dynamics can be leveraged so that a small number of vertices suffices to achieve accurate reconstruction. The present paper broadens...
Single linkage hierarchical clustering is a tool in unsupervised learning which has been fully characterized for finite metric spaces, but not for the unrestricted setting of general networks. We follow a recent line of work to complete the characterization for general networks, and moreover, we provide quantitative bounds on how much information is lost when applying our method to network data. These...
In this work, we present a full-reference stereo image quality assessment algorithm that is based on the sparse representations of luminance images and depth maps. The primary challenge lies in dealing with the sparsity of disparity maps in conjunction with the sparsity of luminance images. Although analysing the sparsity of images is sufficient to bring out the quality of luminance images, the effectiveness...
A multi-stage temporal pooling mechanism is proposed in this paper for improving the prediction capability of an objective quality metric for video quality assessment. The performance of the proposed pooling mechanism is evaluated along with that of traditional pooling mechanisms in terms of linear correlation coefficient and Spearman rank order correlation coefficient on four publicly available video...
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...
The wideband spectrum sensing is vulnerable to interferers and blockers due to inter-modulation distortion (IMD) that arises in nonlinear front-end. Traditional DSP-enhanced receivers use least mean squares (LMS) filters to mitigate IMD. We theoretically study the performance of LMS-based algorithm and give expression of its residual distortion power. We then propose a novel compensation algorithm...
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...
Radar-based automated fall detection systems are considered as an important and emerging technology for elderly assisted living. These radar systems provide non-intrusive sensing capabilities to detect fall events. Various studies have used micro-Doppler signatures to determine falls. However, Doppler radar fall detection systems suffer false alarms stemming from other sudden non-rhythmic motion articulations...
We consider learning problems over training sets in which both, the number of training examples and the dimension of the feature vectors, are large. To solve these problems we propose the random parallel stochastic algorithm (RAPSA). We call the algorithm random parallel because it utilizes multiple processors to operate in a randomly chosen subset of blocks of the feature vector. We call the algorithm...
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...
In recent years, urban mobility demand has become highly variable over time challenging the sustainability of transportation networks of major cities. At the same time, various types of incidents such as accidents, construction zone closures and weather hazards exacerbate the already congested transportation network. Timely detection of such events can offer an unprecedented opportunity to mitigate...
The truncated version of the higher-order singular value decomposition (HOSVD) has a great significance in multi-dimensional tensor-based signal processing. It allows to extract the principal components from noisy observations in order to find a low-rank approximation of the multi-dimensional data. In this paper, we address the question of how good the approximation is by analytically quantifying...
We present a distributed (non-Bayesian) learning algorithm for the problem of parameter estimation with Gaussian noise. The algorithm is expressed as explicit updates on the parameters of the Gaussian beliefs (i.e. means and precision). We show a convergence rate of O(1/k) with the constant term depending on the number of agents and the topology of the network. Moreover, we show almost sure convergence...
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