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# IEEE Transactions on Signal Processing

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• ### Tensor Decomposition for Signal Processing and Machine Learning

Publication Year: 2017, Page(s):3551 - 3582
| | PDF (1166 KB) | HTML Media

Tensors or multiway arrays are functions of three or more indices (i, j, k, . . . )-similar to matrices (two-way arrays), which are functions of two indices (r, c) for (row, column). Tensors have a rich history, stretching over almost a century, and touching upon numerous disciplines; but they have only recently become ubiquitous in signal and data analytics at the confluence of signal processing,... View full abstract»

• ### $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation

Publication Year: 2006, Page(s):4311 - 4322
Cited by:  Papers (2840)  |  Patents (27)
| | PDF (1725 KB) | HTML

In recent years there has been a growing interest in the study of sparse representation of signals. Using an overcomplete dictionary that contains prototype signal-atoms, signals are described by sparse linear combinations of these atoms. Applications that use sparse representation are many and include compression, regularization in inverse problems, feature extraction, and more. Recent activity i... View full abstract»

• ### A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking

Publication Year: 2002, Page(s):174 - 188
Cited by:  Papers (4687)  |  Patents (107)
| | PDF (355 KB) | HTML

Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a physical system. Moreover, it is typically crucial to process data on-line as it arrives, both from the point of view of storage costs as well as for rapid adaptation to changing signal characteristics. In this paper, w... View full abstract»

• ### Variational Mode Decomposition

Publication Year: 2014, Page(s):531 - 544
Cited by:  Papers (132)
| | PDF (3074 KB) | HTML

During the late 1990s, Huang introduced the algorithm called Empirical Mode Decomposition, which is widely used today to recursively decompose a signal into different modes of unknown but separate spectral bands. EMD is known for limitations like sensitivity to noise and sampling. These limitations could only partially be addressed by more mathematical attempts to this decomposition problem, like ... View full abstract»

• ### Zero-forcing methods for downlink spatial multiplexing in multiuser MIMO channels

Publication Year: 2004, Page(s):461 - 471
Cited by:  Papers (1618)  |  Patents (70)
| | PDF (328 KB) | HTML

The use of space-division multiple access (SDMA) in the downlink of a multiuser multiple-input, multiple-output (MIMO) wireless communications network can provide a substantial gain in system throughput. The challenge in such multiuser systems is designing transmit vectors while considering the co-channel interference of other users. Typical optimization problems of interest include the capacity p... View full abstract»

• ### Robust Large Margin Deep Neural Networks

Publication Year: 2017, Page(s):4265 - 4280
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The generalization error of deep neural networks via their classification margin is studied in this paper. Our approach is based on the Jacobian matrix of a deep neural network and can be applied to networks with arbitrary nonlinearities and pooling layers, and to networks with different architectures such as feed forward networks and residual networks. Our analysis leads to the conclusion that a ... View full abstract»

• ### Exploiting Spatial Channel Covariance for Hybrid Precoding in Massive MIMO Systems

Publication Year: 2017, Page(s):3818 - 3832
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We propose a new hybrid precoding technique for massive multi-input multi-output (MIMO) systems using spatial channel covariance matrices in the analog precoder design. Applying a regularized zero-forcing precoder for the baseband precoding matrix, we find an unconstrained analog precoder that maximizes signal-to-leakage-plus-noise ratio (SLNR) while ignoring analog phase shifter constraints. Subs... View full abstract»

• ### Channel Estimation and Performance Analysis of One-Bit Massive MIMO Systems

Publication Year: 2017, Page(s):4075 - 4089
| | PDF (1223 KB) | HTML

This paper considers channel estimation and system performance for the uplink of a single-cell massive multiple-input multiple-output system. Each receiver antenna of the base station is assumed to be equipped with a pair of one-bit analog-to-digital converters to quantize the real and imaginary part of the received signal. We first propose an approach for channel estimation that is applicable for... View full abstract»

• ### Performance Analysis of Source Image Estimators in Blind Source Separation

Publication Year: 2017, Page(s):4166 - 4176
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Blind methods often separate or identify signals or signal subspaces up to an unknown scaling factor. Sometimes it is necessary to cope with the scaling ambiguity, which can be done through reconstructing signals as they are received by sensors, because scales of the sensor responses (images) have known physical interpretations. In this paper, we analyze two approaches that are widely used for com... View full abstract»

• ### Bayesian Compressive Sensing

Publication Year: 2008, Page(s):2346 - 2356
Cited by:  Papers (767)  |  Patents (3)
| | PDF (1561 KB) | HTML

The data of interest are assumed to be represented as N-dimensional real vectors, and these vectors are compressible in some linear basis B, implying that the signal can be reconstructed accurately using only a small number M Lt N of basis-function coefficients associated with B. Compressive sensing is a framework whereby one does not measure one of the aforementioned N-dimensional signals directl... View full abstract»

• ### Secure Communications for Dual-Polarized MIMO Systems

Publication Year: 2017, Page(s):4177 - 4192
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To enhance secure communications, we deploy the dual-polarized antenna arrays at communication nodes of the multi-input multioutput (MIMO) system, where the base station communicates with multiple legitimate users in the presence of an eavesdropper. We also adopt the dual-structured precoding in which a preprocessing matrix based on the polarized array spatial correlation and a linear precoding ba... View full abstract»

• ### AMP-Inspired Deep Networks for Sparse Linear Inverse Problems

Publication Year: 2017, Page(s):4293 - 4308
| | PDF (1559 KB) | HTML

Deep learning has gained great popularity due to its widespread success on many inference problems. We consider the application of deep learning to the sparse linear inverse problem, where one seeks to recover a sparse signal from a few noisy linear measurements. In this paper, we propose two novel neural-network architectures that decouple prediction errors across layers in the same way that the ... View full abstract»

• ### Scalable and Flexible Multiview MAX-VAR Canonical Correlation Analysis

Publication Year: 2017, Page(s):4150 - 4165
| | PDF (1590 KB) | HTML

Generalized canonical correlation analysis (GCCA) aims at finding latent low-dimensional common structure from multiple views (feature vectors in different domains) of the same entities. Unlike principal component analysis that handles a single view, (G)CCA is able to integrate information from different feature spaces. Here we focus on MAX-VAR GCCA, a popular formulation that has recently gained ... View full abstract»

• ### Near-Optimal Hybrid Processing for Massive MIMO Systems via Matrix Decomposition

Publication Year: 2017, Page(s):3922 - 3933
| | PDF (837 KB) | HTML

For practical implementation of massive multiple-input multiple-output (MIMO) systems, the hybrid processing (precoding/combining) structure is promising to reduce the high implementation cost and power consumption rendered by large number of radio frequency (RF) chains of the traditional processing structure. The hybrid processing is realized through low-dimensional digital baseband processing co... View full abstract»

• ### A Generalized Memory Polynomial Model for Digital Predistortion of RF Power Amplifiers

Publication Year: 2006, Page(s):3852 - 3860
Cited by:  Papers (422)  |  Patents (18)
| | PDF (675 KB) | HTML

Conventional radio-frequency (RF) power amplifiers operating with wideband signals, such as wideband code-division multiple access (WCDMA) in the Universal Mobile Telecommunications System (UMTS) must be backed off considerably from their peak power level in order to control out-of-band spurious emissions, also known as "spectral regrowth." Adapting these amplifiers to wideband operation therefore... View full abstract»

• ### Robust Distributed Estimation by Networked Agents

Publication Year: 2017, Page(s):3909 - 3921
| | PDF (1150 KB) | HTML

Diffusion adaptive networks tasked with solving estimation problems have attracted attention in recent years due to their reliability, scalability, resource efficiency, and resilience to node and link failure. Diffusion adaptation strategies that are based on the least-mean-squares algorithm can be nonrobust against impulsive noise corrupting the measurements. Impulsive noise can degrade stability... View full abstract»

• ### Subspace Learning From Bits

Publication Year: 2017, Page(s):4429 - 4442
| | PDF (1582 KB) | HTML

Networked sensing, where the goal is to perform complex inference using a large number of inexpensive and decentralized sensors, has become an increasingly attractive research topic due to its applications in wireless sensor networks and internet-of-things. To reduce the communication, sensing, and storage complexity, this paper proposes a simple sensing and estimation framework to faithfully reco... View full abstract»

• ### Majorization-Minimization Algorithms in Signal Processing, Communications, and Machine Learning

Publication Year: 2017, Page(s):794 - 816
Cited by:  Papers (1)
| | PDF (1188 KB) | HTML

This paper gives an overview of the majorization-minimization (MM) algorithmic framework, which can provide guidance in deriving problem-driven algorithms with low computational cost. A general introduction of MM is presented, including a description of the basic principle and its convergence results. The extensions, acceleration schemes, and connection to other algorithmic frameworks are also cov... View full abstract»

• ### The Gaussian Mixture Probability Hypothesis Density Filter

Publication Year: 2006, Page(s):4091 - 4104
Cited by:  Papers (551)  |  Patents (5)
| | PDF (1066 KB) | HTML

A new recursive algorithm is proposed for jointly estimating the time-varying number of targets and their states from a sequence of observation sets in the presence of data association uncertainty, detection uncertainty, noise, and false alarms. The approach involves modelling the respective collections of targets and measurements as random finite sets and applying the probability hypothesis densi... View full abstract»

• ### A sparse signal reconstruction perspective for source localization with sensor arrays

Publication Year: 2005, Page(s):3010 - 3022
Cited by:  Papers (739)  |  Patents (3)
| | PDF (704 KB) | HTML

We present a source localization method based on a sparse representation of sensor measurements with an overcomplete basis composed of samples from the array manifold. We enforce sparsity by imposing penalties based on the ℓ1-norm. A number of recent theoretical results on sparsifying properties of ℓ1 penalties justify this choice. Explicitly enforcing the sparsit... View full abstract»

• ### Particle filters for positioning, navigation, and tracking

Publication Year: 2002, Page(s):425 - 437
Cited by:  Papers (697)  |  Patents (18)
| | PDF (335 KB) | HTML

A framework for positioning, navigation, and tracking problems using particle filters (sequential Monte Carlo methods) is developed. It consists of a class of motion models and a general nonlinear measurement equation in position. A general algorithm is presented, which is parsimonious with the particle dimension. It is based on marginalization, enabling a Kalman filter to estimate all position de... View full abstract»

• ### Matching pursuits with time-frequency dictionaries

Publication Year: 1993, Page(s):3397 - 3415
Cited by:  Papers (3821)  |  Patents (115)
| | PDF (2488 KB)

The authors introduce an algorithm, called matching pursuit, that decomposes any signal into a linear expansion of waveforms that are selected from a redundant dictionary of functions. These waveforms are chosen in order to best match the signal structures. Matching pursuits are general procedures to compute adaptive signal representations. With a dictionary of Gabor functions a matching pursuit d... View full abstract»

• ### Super-Resolution Delay-Doppler Estimation for OFDM Passive Radar

Publication Year: 2017, Page(s):2197 - 2210
Cited by:  Papers (1)
| | PDF (1297 KB) | HTML

In this paper, we consider the problem of joint delay-Doppler estimation of moving targets in a passive radar that makes use of orthogonal frequency-division multiplexing communication signals. A compressed sensing algorithm is proposed to achieve supper resolution and better accuracy, using both the atomic norm and the ℓ1-norm. The atomic norm is used to manifest the signal spar... View full abstract»

• ### Iterative Constrained Weighted Least Squares Source Localization Using TDOA and FDOA Measurements

Publication Year: 2017, Page(s):3990 - 4003
| | PDF (1165 KB) | HTML

This paper investigates the constrained weighted least squares (CWLS) source localization problem by using time difference of arrival and frequency difference of arrival measurements. The problem can be formulated as a quadratic programming with two indefinite quadratic equality constraints, which is nonconvex and NP-hard. Moreover, the weighting matrix is coupled with the unknown source position ... View full abstract»

• ### Efficient L1-Norm Principal-Component Analysis via Bit Flipping

Publication Year: 2017, Page(s):4252 - 4264
| | PDF (1312 KB) | HTML

It was shown recently that the K L1-norm principal components (L1-PCs) of a real-valued data matrix X ∈ R D×N (N data samples of D dimensions) can be exactly calculated with cost O(2NK) or, when advantageous, O(NdK - K + 1) where d=rank (X), K<;d. In applications where X is large (e.g., “big” data of large N and/or “heavyȁ... View full abstract»

• ### Correntropy: Properties and Applications in Non-Gaussian Signal Processing

Publication Year: 2007, Page(s):5286 - 5298
Cited by:  Papers (269)
| | PDF (682 KB) | HTML

The optimality of second-order statistics depends heavily on the assumption of Gaussianity. In this paper, we elucidate further the probabilistic and geometric meaning of the recently defined correntropy function as a localized similarity measure. A close relationship between correntropy and M-estimation is established. Connections and differences between correntropy and kernel methods are present... View full abstract»

• ### Structured Compressed Sensing: From Theory to Applications

Publication Year: 2011, Page(s):4053 - 4085
Cited by:  Papers (341)  |  Patents (8)
| | PDF (1987 KB) | HTML

Compressed sensing (CS) is an emerging field that has attracted considerable research interest over the past few years. Previous review articles in CS limit their scope to standard discrete-to-discrete measurement architectures using matrices of randomized nature and signal models based on standard sparsity. In recent years, CS has worked its way into several new application areas. This, in turn, ... View full abstract»

• ### An Iteratively Weighted MMSE Approach to Distributed Sum-Utility Maximization for a MIMO Interfering Broadcast Channel

Publication Year: 2011, Page(s):4331 - 4340
Cited by:  Papers (342)  |  Patents (1)
| | PDF (828 KB) | HTML

Consider the multiple-input multiple-output (MIMO) interfering broadcast channel whereby multiple base stations in a cellular network simultaneously transmit signals to a group of users in their own cells while causing interference to each other. The basic problem is to design linear beamformers that can maximize the system throughput. In this paper, we propose a linear transceiver design algorith... View full abstract»

• ### Joint Beamforming and Power-Splitting Control in Downlink Cooperative SWIPT NOMA Systems

Publication Year: 2017, Page(s):4874 - 4886
| | PDF (1140 KB) | HTML

This paper investigates the application of simultaneous wireless information and power transfer (SWIPT) to cooperative non-orthogonal multiple access (NOMA). A new cooperative multiple-input single-output (MISO) SWIPT NOMA protocol is proposed, where a user with a strong channel condition acts as an energy-harvesting (EH) relay by adopting power splitting (PS) scheme to help a user with a poor cha... View full abstract»

• ### Parallel and Distributed Methods for Constrained Nonconvex Optimization—Part I: Theory

Publication Year: 2017, Page(s):1929 - 1944
Cited by:  Papers (2)
| | PDF (345 KB) | HTML Media

In this two-part paper, we propose a general algorithmic framework for the minimization of a nonconvex smooth function subject to nonconvex smooth constraints, and also consider extensions to some structured, nonsmooth problems. The algorithm solves a sequence of (separable) strongly convex problems and maintains feasibility at each iteration. Convergence to a stationary solution of the original n... View full abstract»

• ### Adaptive Low-Rank Matrix Completion

Publication Year: 2017, Page(s):3603 - 3616
| | PDF (1090 KB) | HTML Media

The low-rank matrix completion problem is fundamental to a number of tasks in data mining, machine learning, and signal processing. This paper considers the problem of adaptive matrix completion in time-varying scenarios. Given a sequence of incomplete and noise-corrupted matrices, the goal is to recover and track the underlying low rank matrices. Motivated from the classical least-mean square (LM... View full abstract»

• ### Sparse Reconstruction by Separable Approximation

Publication Year: 2009, Page(s):2479 - 2493
Cited by:  Papers (581)  |  Patents (1)
| | PDF (763 KB) | HTML

Finding sparse approximate solutions to large underdetermined linear systems of equations is a common problem in signal/image processing and statistics. Basis pursuit, the least absolute shrinkage and selection operator (LASSO), wavelet-based deconvolution and reconstruction, and compressed sensing (CS) are a few well-known areas in which problems of this type appear. One standard approach is to m... View full abstract»

• ### Novel Low-Density Signature for Synchronous CDMA Systems Over AWGN Channel

Publication Year: 2008, Page(s):1616 - 1626
Cited by:  Papers (96)  |  Patents (2)
| | PDF (1540 KB) | HTML

Novel low-density signature (LDS) structure is proposed for transmission and detection of symbol-synchronous communication over memoryless Gaussian channel. Given N as the processing gain, under this new arrangement, users' symbols are spread over N chips but virtually only dv < N chips that contain nonzero-values. The spread symbol is then so uniquely interleaved as the sampled, at ... View full abstract»

• ### Discrete Signal Processing on Graphs

Publication Year: 2013, Page(s):1644 - 1656
Cited by:  Papers (137)
| | PDF (2577 KB) | HTML

In social settings, individuals interact through webs of relationships. Each individual is a node in a complex network (or graph) of interdependencies and generates data, lots of data. We label the data by its source, or formally stated, we index the data by the nodes of the graph. The resulting signals (data indexed by the nodes) are far removed from time or image signals indexed by well ordered ... View full abstract»

• ### Direct Localization for Massive MIMO

Publication Year: 2017, Page(s):2475 - 2487
| | PDF (941 KB) | HTML

Large-scale MIMO systems are well known for their advantages in communications, but they also have the potential for providing very accurate localization, thanks to their high angular resolution. A difficult problem arising indoors and outdoors is localizing users over multipath channels. Localization based on angle of arrival (AOA) generally involves a two-step procedure, where signals are first ... View full abstract»

• ### Parallel and Distributed Methods for Constrained Nonconvex Optimization-Part II: Applications in Communications and Machine Learning

Publication Year: 2017, Page(s):1945 - 1960
Cited by:  Papers (1)
| | PDF (1103 KB) | HTML Media

In Part I of this paper, we proposed and analyzed a novel algorithmic framework for the minimization of a nonconvex objective function, subject to nonconvex constraints, based on inner convex approximations. This Part II is devoted to the (nontrivial) application of the framework to the following relevant large-scale problems ranging from communications to machine learning: 1) (generalizations of)... View full abstract»

• ### Analog Beamforming in MIMO Communications With Phase Shift Networks and Online Channel Estimation

Publication Year: 2010, Page(s):4131 - 4143
Cited by:  Papers (97)  |  Patents (3)
| | PDF (1375 KB) | HTML

In multiple-input multiple-output (MIMO) systems, the use of many radio frequency (RF) and analog-to-digital converter (ADC) chains at the receiver is costly. Analog beamformers operating in the RF domain can reduce the number of antenna signals to a feasible number of baseband channels. Subsequently, digital beamforming is used to capture the desired user signal. In this paper, we consider the de... View full abstract»

• ### Distributed Adaptive Learning of Graph Signals

Publication Year: 2017, Page(s):4193 - 4208
| | PDF (1318 KB) | HTML

The aim of this paper is to propose distributed strategies for adaptive learning of signals defined over graphs. Assuming the graph signal to be bandlimited, the method enables distributed reconstruction, with guaranteed performance in terms of mean-square error, and tracking from a limited number of sampled observations taken from a subset of vertices. A detailed mean-square analysis is carried o... View full abstract»

• ### Orthogonal Frequency Division Multiplexing With Index Modulation

Publication Year: 2013, Page(s):5536 - 5549
Cited by:  Papers (65)
| | PDF (2852 KB) | HTML

In this paper, a novel orthogonal frequency division multiplexing (OFDM) scheme, called OFDM with index modulation (OFDM-IM), is proposed for operation over frequency-selective and rapidly time-varying fading channels. In this scheme, the information is conveyed not only by M-ary signal constellations as in classical OFDM, but also by the indices of the subcarriers, which are activated according t... View full abstract»

• ### LLR-Based Successive Cancellation List Decoding of Polar Codes

Publication Year: 2015, Page(s):5165 - 5179
Cited by:  Papers (25)
| | PDF (4241 KB) | HTML

We show that successive cancellation list decoding can be formulated exclusively using log-likelihood ratios. In addition to numerical stability, the log-likelihood ratio based formulation has useful properties that simplify the sorting step involved in successive cancellation list decoding. We propose a hardware architecture of the successive cancellation list decoder in the log-likelihood ratio ... View full abstract»

Publication Year: 2017, Page(s):3948 - 3959
| | PDF (847 KB) | HTML

The paper is concerned with stabilizing the family of adaptive filtering algorithms based on minimizing the 2Lth moment of the estimation error, with L being an integer greater than 1. Stabilization is attained via a proposed normalization of the algorithm. Mean square stability of the normalized algorithm is proved for a Markov plant for all L > 1. Transient and steady-state performances of th... View full abstract»

• ### Maximum-Likelihood Detection for MIMO Systems Based on Differential Metrics

Publication Year: 2017, Page(s):3718 - 3732
| | PDF (1157 KB) | HTML

The multiple-input multiple-output (MIMO) system makes efficient use of spectrum and increases the transmission throughput in wireless communications. The sphere decoding (SD) is an efficient algorithm that enables the maximum-likelihood (ML) detection for the MIMO system. However, the SD algorithm has variable complexity, and its complexity increases rapidly with decreasing signal-to-noise ratio ... View full abstract»

• ### Sparse Sensing With Co-Prime Samplers and Arrays

Publication Year: 2011, Page(s):573 - 586
Cited by:  Papers (202)  |  Patents (2)
| | PDF (975 KB) | HTML

This paper considers the sampling of temporal or spatial wide sense stationary (WSS) signals using a co-prime pair of sparse samplers. Several properties and applications of co-prime samplers are developed. First, for uniform spatial sampling with M and N sensors where M and N are co-prime with appropriate interelement spacings, the difference co-array has O(MN) freedoms which can be exploited in ... View full abstract»

• ### Empirical Wavelet Transform

Publication Year: 2013, Page(s):3999 - 4010
Cited by:  Papers (92)  |  Patents (1)
| | PDF (3301 KB) | HTML

Some recent methods, like the empirical mode decomposition (EMD), propose to decompose a signal accordingly to its contained information. Even though its adaptability seems useful for many applications, the main issue with this approach is its lack of theory. This paper presents a new approach to build adaptive wavelets. The main idea is to extract the different modes of a signal by designing an a... View full abstract»

• ### Dynamic Resource Allocation for Energy Efficient Transmission in Digital Subscriber Lines

Publication Year: 2017, Page(s):4353 - 4366
| | PDF (1356 KB) | HTML

Linear matrix precoding, also known as vectoring, is a well-known technique to mitigate multiuser interference in the downlink digital subscriber line (DSL) transmission. While effective in canceling interference, vectoring does incur major communication overhead and computational overhead in terms of the transmission of idle symbols and precoder-data multiplications at each data frame, resulting ... View full abstract»

• ### Nonuniform fast Fourier transforms using min-max interpolation

Publication Year: 2003, Page(s):560 - 574
Cited by:  Papers (419)  |  Patents (4)
| | PDF (879 KB) | HTML

The fast Fourier transform (FFT) is used widely in signal processing for efficient computation of the FT of finite-length signals over a set of uniformly spaced frequency locations. However, in many applications, one requires nonuniform sampling in the frequency domain, i.e., a nonuniform FT. Several papers have described fast approximations for the nonuniform FT based on interpolating an oversamp... View full abstract»

• ### Online Categorical Subspace Learning for Sketching Big Data with Misses

Publication Year: 2017, Page(s):4004 - 4018
| | PDF (1278 KB) | HTML

With the scale of data growing every day, reducing the dimensionality (a.k.a. sketching) of high-dimensional data has emerged as a task of paramount importance. Relevant issues to address in this context include the sheer volume of data that may consist of categorical observations, the typically streaming format of acquisition, and the possibly missing entries. To cope with these challenges, this ... View full abstract»

• ### Hankel Matrix Nuclear Norm Regularized Tensor Completion for $N$-dimensional Exponential Signals

Publication Year: 2017, Page(s):3702 - 3717
| | PDF (2820 KB) | HTML

Signals are generally modeled as a superposition of exponential functions in spectroscopy of chemistry, biology, and medical imaging. For fast data acquisition or other inevitable reasons, however, only a small amount of samples may be acquired, and thus, how to recover the full signal becomes an active research topic, but existing approaches cannot efficiently recover View full abstract»

• ### Stationary Signal Processing on Graphs

Publication Year: 2017, Page(s):3462 - 3477
| | PDF (1410 KB) | HTML

Graphs are a central tool in machine learning and information processing as they allow to conveniently capture the structure of complex datasets. In this context, it is of high importance to develop flexible models of signals defined over graphs or networks. In this paper, we generalize the traditional concept of wide sense stationarity to signals defined over the vertices of arbitrary weighted un... View full abstract»

• ### Low-Rank Phase Retrieval

Publication Year: 2017, Page(s):4059 - 4074
| | PDF (747 KB) | HTML Media

We develop two iterative algorithms for solving the low-rank phase retrieval (LRPR) problem. LRPR refers to recovering a low-rank matrix X from magnitude-only (phaseless) measurements of random linear projections of its columns. Both methods consist of a spectral initialization step followed by an iterative algorithm to maximize the observed data likelihood. We obtain sample complexity bounds for ... View full abstract»

## Aims & Scope

IEEE Transactions on Signal Processing covers novel theory, algorithms, performance analyses and applications of techniques for the processing, understanding, learning, retrieval, mining, and extraction of information from signals

Full Aims & Scope

## Meet Our Editors

Editor-in-Chief
Sergios Theodoridis
University of Athens