IEEE Journal of Selected Topics in Signal Processing

Systems that capture and process analog signals must first acquire them through an analog-to-digital converter. While subsequent digital processing can remove statistical correlations present in the acquired data, the dynamic range of the converter is typically scaled to match that of the input analog signal. The present paper develops an approach for analog-to-digital conversion that aims at mini... View full abstract»

We deal with zero-delay source coding of a vector-valued Gauss-Markov source subject to a mean-squared error (MSE) fidelity criterion characterized by the operational zero-delay vector-valued Gaussian rate distortion function (RDF). We address this problem by considering the nonanticipative RDF (NRDF), which is a lower bound to the causal optimal performance theoretically attainable function (or s... View full abstract»

Bayesian approximate message passing (BAMP) is an efficient method in compressed sensing that is nearly optimal in the minimum mean squared error (MMSE) sense. Multiple measurement vector (MMV)-BAMP performs joint recovery of multiple vectors with identical support and accounts for correlations in the signal of interest and in the noise. In this paper, we show how to reduce the complexity of vecto... View full abstract»

This paper considers the problem of acquiring an unknown target location (among a finite number of locations) via a sequence of measurements, where each measurement consists of simultaneously probing a group of locations. The resulting observation consists of a sum of an indicator of the target's presence in the probed region, and a zero mean Gaussian noise term whose variance is a function of the... View full abstract»

We propose a novel, information-theoretic method, called MaxEnt, for efficient data acquisition for low-rank matrix recovery. This proposed method has important applications to a wide range of problems, including image processing and text document indexing. Fundamental to our design approach is the so-called maximum entropy principle, which states that the measurement masks that maximize the entro... View full abstract»

The group testing problem consists of determining a small set of defective items from a larger set of items based on a number of tests, and is relevant in applications such as medical testing, communication protocols, pattern matching, and more. In this paper, we revisit an efficient algorithm for noisy group testing in which each item is decoded separately (Malyutov and Mateev, 1980), and develop... View full abstract»

Multipath matching pursuit (MMP) is a recent extension of the orthogonal matching pursuit algorithm that recovers sparse signals with a tree-searching strategy. In this paper, we present a new analysis for the MMP algorithm using the restricted isometry property. Our result shows that if the sampling matrix A ∈ Rm×nsatisfies the RIP of order K + L with isometry constant δK + L View full abstract»

We consider the problem of universal joint clustering and registration of images. Image clustering focuses on grouping similar images, while image registration refers to the task of aligning copies of an image that have been subject to rigid-body transformations, such as rotations and translations. We first study registering two images using maximum mutual information and prove its asymptotic opti... View full abstract»

The community detection problem involves making inferences about node labels in a graph, based on observing the graph edges. This paper studies the effect of additional, nongraphical side information on the phase transition of exact recovery in the binary stochastic block model withnnodes. When side information consists of noisy labels with error probability α, it is shown that phase transi... View full abstract»

Spectral clustering is a celebrated algorithm that partitions the objects based on pairwise similarity information. While this approach has been successfully applied to a variety of domains, it comes with limitations. The reason is that there are many other applications in which only multiway similarity measures are available. This motivates us to explore the multiway measurement setting. In this ... View full abstract»

We consider the problem of aggregating pairwise comparisons to obtain a consensus ranking order over a collection of objects. We use the popular Bradley-Terry-Luce (BTL) model, which allows us to probabilistically describe pairwise comparisons between objects. In particular, we employ the Bayesian BTL model, which allows for meaningful prior assumptions and to cope with situations where the number... View full abstract»

Suppose one aims to identify only the top-K among a large collection of n items provided M-wise comparison data, where a set of M items in each data sample are ranked in order of individual preference. Natural questions that arise are as follows: 1) how one can reliably achieve the top-K rank aggregation task; and 2) how many M-wise samples one needs to achieve it. In this paper, we answer these t... View full abstract»

We investigate the nonparametric, composite hypothesis testing problem for arbitrary unknown distributions in the asymptotic regime where both the sample size and the number of hypothesis grow exponentially large. Such asymptotic analysis is important in many practical problems, where the number of variations that can exist within a family of distributions can be countably infinite. We introduce t... View full abstract»

We study the classification performance of Kronecker-structured (K-S) subpsace models in two asymptotic regimes and develop an algorithm for fast and compact K-S subspace learning for better classification and representation of multidimensional signals by exploiting the structure in the signal. First, we study the classification performance in terms of diversity order and pairwise geometry of the ... View full abstract»

We investigate the problem of estimating the causal effect of a treatment on individual subjects from observational data; this is a central problem in various application domains, including healthcare, social sciences, and online advertising. Within the Neyman-Rubin potential outcomes model, we use the Kullback-Leibler (KL) divergence between the estimated and true distributions as a measure of ac... View full abstract»

This paper derives sufficient conditions for local recovery of coordinate dictionaries comprising a Kronecker-structured dictionary that is used for representing Kth-order tensor data. Tensor observations are assumed to be generated from a Kronecker-structured dictionary multiplied by sparse coefficient tensors that follow the separable sparsity model. This paper provides sufficient conditions on ... View full abstract»

This paper investigates, from information theoretic grounds, a learning problem based on the principle that any regularity in a given dataset can be exploited to extract compact features from data, i.e., using fewer bits than needed to fully describe the data itself, in order to build meaningful representations of a relevant content (multiple labels). We begin studying a multitask learning (MTL) p... View full abstract»

We study detection of random signals corrupted by noise that over time switch their values (states) between a finite set of possible values, where the switchings occur at unknown points in time. We model such signals as hidden semi-Markov signals, which generalize classical Markov chains by introducing explicit (possibly nongeometric) distribution for the time spent in each state. Assuming two pos... View full abstract»

We study a fine-grained model in which a perturbed version of some data (D) is to be disclosed, with the aims of permitting the receiver to accurately infer some useful aspects (X = f(D)) of it, while preventing her from inferring other private aspects (Y = g(D)). Correlation between the bases for these inferences necessitates compromise between these goals. Determining exactly how the disclosure ... View full abstract»

Non-discrimination is a recognized objective in algorithmic decision making. In this paper, we introduce a novel probabilistic formulation of data pre-processing for reducing discrimination. We propose a convex optimization for learning a data transformation with three goals: controlling group discrimination, limiting distortion in individual data samples, and preserving utility. Several theoretic... View full abstract»