34 accepted papers
Signal Alignment has been recently introduced as a simple and effective means of communicating under powerful and unpredictable (e.g., intermittent) interference. Signal alignment uses repetition coding at the transmitter and canonical correlation analysis (CCA) at the (multi-antenna) receiver to sy…
Given an unweighted, undirected, and simple graph, the Densest k-Subgraph (DkS) problem aims to find a subgraph of k vertices that has the maximum average induced degree. In this paper, we consider an equivalent reformulation of the DkS problem via diagonal loading. On relaxing the combinatorial con…
Hybrid automatic repeat request (HARQ) protocols use forward error correction and detection to trigger additional transmissions, until a packet is correctly decoded. Type-I HARQ (HARQ-I) uses packet retransmission and so-called chase combining of the received packets. HARQ protocols are widely used…
In this paper we revisit a recently proposed underlay communication scheme which relies on repetition of the secondary signal at the transmitter and canonical correlation analysis CCA at the (multi-antenna) receiver. In this setting, CCA can provably extract the underlay signal in the presence of po…
We consider the problem of learning smooth multivariate probability density functions. We invoke the canonical decomposition of multivariate functions and we show that if a joint probability density function admits a truncated Fourier series representation, then the classical univariate Fejér-Riesz…
Dense subgraph discovery (DSD) is a key primitive in graph mining that typically deals with extracting cliques and near-cliques. In this paper, we revisit the optimal quasi-clique (OQC) formulation for DSD and establish that it is NP--hard. In addition, we reveal the hitherto unknown property that O…
Patient enrollment is critical to the success of a clinical trial. In practice, before launching a trial, one of the top priorities is to predict the enrollment rate for different countries, so that one can select clinical sites from the countries with the highest enrollment rates to accelerate pati…
Antenna arrays with a large number of sensors are becoming increasingly common in radio astronomy. This has motivated the development of array signal processing tools for high-resolution imaging that exploit source signal properties such as sparsity and spectral or temporal variability. We propose a…
We introduce the triangle-densest-K-subgraph problem (TDKS) for undirected graphs: given a size parameter K, compute a subset of K vertices that maximizes the number of induced triangles. The problem corresponds to the simplest generalization of the edge based densest-K-subgraph problem (DKS) to the…
The spectrum underlay concept promises enhanced spectrum utilization without disturbing legacy / licensed or scientific primary users, so long as their interference constraints can be met. Existing underlay schemes assume that both the primary signal to secondary interference plus noise ratio, and t…
Accurate prediction of the transmission of epidemic diseases such as COVID-19 is crucial for implementing effective mitigation measures. In this work, we develop a tensor method to predict the evolution of epidemic trends for many regions simultaneously. We construct a 3-way spatio-temporal tensor (…
Matrix factorization (MF) plays an important role in a wide range of machine learning and data mining models. MF is commonly used to obtain item embeddings and feature representations due to its ability to capture correlations and higher-order statistical dependencies across dimensions. In many appl…
The Canonical Polyadic Decomposition (CPD) is one of the most basic tensor models used in signal processing and machine learning. Despite its wide applicability, identifiability conditions and algorithms for CPD in cases where the tensor is incomplete are lagging behind its practical use. We first p…
We consider the NP-hard combinatorial optimization problem of minimizing arbitrary quadratic forms over the {0, 1 } (Boolean) lattice. While polynomial-time approximation algorithms do exist for such problems, they suffer from the practical drawback of being computationally involved - often a side e…
Predicting the response of cancer cells to drugs is an important problem in pharmacogenomics. Recent efforts in generation of large scale datasets profiling gene expression and drug sensitivity in cell lines have provided a unique opportunity to study this problem. However, one major challenge is th…
We study the problem of learning a mixture model of non-parametric product distributions. The problem of learning a mixture model is that of finding the component distributions along with the mixing weights using observed samples generated from the mixture. The problem is well-studied in the paramet…
Sampling lies at the heart of signal processing. The celebrated Shan-non - Nyquist theorem states that in order to reconstruct a continuous or discrete time signal from uniform samples one must sample at a rate twice the highest frequency present in the signal. Numerous signals and images of interes…
Quadratically constrained quadratic programming (QCQP) forms an important class of optimization tasks in various engineering disciplines. Fast identification of a feasible point under low computational complexity load is critical for several approximation techniques which have been developed to solv…
This work focuses on the problem of fusing a hyperspectral image (HSI) and a multispectral image (MSI) to produce a super-resolution image that admits high spatial and spectral resolutions. Existing algorithms are mostly based on joint low-rank factorization of the ma-tricized HSI and MSI. This fram…
The sum-of-correlations (SUMCOR) generalized canonical correlation analysis (GCCA) aims at producing low-dimensional representations of multiview data via enforcing pairwise similarity of the reduced-dimension views. SUMCOR has been applied to a large variety of applications including blind separati…
Energy disaggregation is the task of decomposing the aggregated power consumption readings of a household into its constituent parts. In this paper, we propose a supervised, non-parametric framework for energy disaggregation. We demonstrate that the problem is equivalent to maximizing a set-function…
The 3GPP suggests to combine dual polarized (DP) antenna arrays with the double directional (DD) channel model for downlink channel estimation. This combination strikes a good balance between high-capacity communications and parsimonious channel modeling, and also brings limited feedback schemes for…
Quadratically Constrained Quadratic Programming (QCQP) is NP-hard in its general non-convex form, but it frequently arises in engineering design and applications ranging from state estimation to beamforming and clustering. Several polynomial-time approximation algorithms exist for non-convex QCQP pr…
We consider the problem of nonnegative tensor factorization. Our aim is to derive an efficient algorithm that is also suitable for parallel implementation. We adopt the alternating optimization (AO) framework and solve each matrix nonnegative least-squares problem via a Nesterov-type algorithm for s…
Owing to the rapidly increasing traffic demands on satellite connectivity, the current exclusive frequency allocation is becoming obsolete. Instead, aggressive frequency reuse and interference mitigation techniques are promising ideas that both industry and academia are investigating. This paper pro…
Unlike dimensionality reduction (DR) tools for single-view data, e.g., principal component analysis (PCA), canonical correlation analysis (CCA) and generalized CCA (GCCA) are able to integrate information from multiple feature spaces of data. This is critical in multi-modal data fusion and analytics…
Most learning approaches treat dimensionality reduction (DR) and clustering separately (i.e., sequentially), but recent research has shown that optimizing the two tasks jointly can substantially improve the performance of both. The premise behind the latter genre is that the data samples are obtaine…
Phase retrieval has recently attracted renewed interest. It is revisited here through a new approach based on nonconvex quadratically constrained quadratic programming (QCQP). A least-squares (LS) formulation is adopted, and a recently developed non-convex QCQP approximation technique called feasibl…
This paper considers phase retrieval from the magnitude of 1-D oversampled Fourier measurements. We first revisit the well-known lack of identifiability in this case, and point out that there always exists a solution that is minimum phase, even though the desired signal is not. Next, we explain how…
Estimating the power spectrum of a wide-sense stationary stochastic process is a core component of several signal processing tasks. Distributed spectrum sensing problems naturally emerge in cases where measurements of different realizations of a stochastic process are collected at multiple spatial l…
This paper focuses on volume minimization (VolMin)-based structured matrix factorization (SMF), which factors a data matrix into a full-column rank basis and a coefficient matrix whose columns reside in the unit simplex. The VolMin criterion achieves this goal via finding a minimum-volume enclosing…
Multicast beamforming is a part of the Evolved Multimedia Broadcast Multicast Service (eMBMS) in the Long-Term Evolution (LTE) standard for efficient audio and video streaming. The associated beamformer design problem has drawn considerable attention over the last decade, but existing solutions are…
Most tensor decomposition algorithms were developed for in-memory computation on a single machine. There are a few recent exceptions that were designed for parallel and distributed computation, but these cannot easily incorporate practically important constraints, such as nonnegativity. A new constr…
Wideband spectrum sensing is one of the core components of cognitive radio. A novel frugal sensing scheme was recently proposed by Mehanna et al, aiming to crowdsource spectrum sensing operations to a network of sensors transmitting randomly filtered power measurement bits to a fusion center (FC). T…