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IEEE Journal of Selected Topics in Signal Processing

Issue 4 • June 2016

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Displaying Results 1 - 25 of 25
  • Front Cover

    Publication Year: 2016, Page(s): C1
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  • IEEE Signal Processing Society

    Publication Year: 2016, Page(s): C2
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  • Table of Contents

    Publication Year: 2016, Page(s):603 - 604
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  • Introduction to the Issue on Structured Matrices in Signal and Data Processing

    Publication Year: 2016, Page(s):605 - 607
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  • An Overview of Low-Rank Matrix Recovery From Incomplete Observations

    Publication Year: 2016, Page(s):608 - 622
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (408 KB) | HTML iconHTML

    Low-rank matrices play a fundamental role in modeling and computational methods for signal processing and machine learning. In many applications where low-rank matrices arise, these matrices cannot be fully sampled or directly observed, and one encounters the problem of recovering the matrix given only incomplete and indirect observations. This paper provides an overview of modern techniques for e... View full abstract»

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  • A Characterization of Deterministic Sampling Patterns for Low-Rank Matrix Completion

    Publication Year: 2016, Page(s):623 - 636
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1183 KB) | HTML iconHTML

    Low-rank matrix completion (LRMC) problems arise in a wide variety of applications. Previous theory mainly provides conditions for completion under missing-at-random samplings. This paper studies deterministic conditions for completion. An incomplete d × N matrix is finitely rank-r completable if there are at most finitely many rank-r matrices that agree with all its observed entries. Finit... View full abstract»

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  • Hankel Low-Rank Matrix Completion: Performance of the Nuclear Norm Relaxation

    Publication Year: 2016, Page(s):637 - 646
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (600 KB) | HTML iconHTML

    The completion of matrices with missing values under the rank constraint is a nonconvex optimization problem. A popular convex relaxation is based on minimization of the nuclear norm (sum of singular values) of the matrix. For this relaxation, an important question is whether the two optimization problems lead to the same solution. This question was addressed in the literature mostly in the case o... View full abstract»

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  • Structured Low-Rank Matrix Factorization for Haplotype Assembly

    Publication Year: 2016, Page(s):647 - 657
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (996 KB) | HTML iconHTML

    In matrix factorization problems, one seeks to decompose a data matrix into a product of two matrices-frequently, one captures meaningful information contained in the data, and the other specifies how this information is combined to generate the data matrix. In this paper, matrix factorization that arises in haplotype assembly, an important NP-hard problem in genomics, is studied. Haplotypes are s... View full abstract»

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  • Off-the-Grid Low-Rank Matrix Recovery and Seismic Data Reconstruction

    Publication Year: 2016, Page(s):658 - 671
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (834 KB) | HTML iconHTML

    Matrix sensing problems capitalize on the knowledge that a data matrix of interest exhibits low rank properties. This low dimensional structure often arises because the data matrix is obtained by sampling a smooth function on a regular (or structured) grid. However, in many practical situations the measurements are taken on an irregular grid (that is accurately known). This results in an “u... View full abstract»

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  • Beyond Low Rank + Sparse: Multiscale Low Rank Matrix Decomposition

    Publication Year: 2016, Page(s):672 - 687
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3557 KB) | HTML iconHTML Multimedia Media

    We present a natural generalization of the recent low rank + sparse matrix decomposition and consider the decomposition of matrices into components of multiple scales. Such decomposition is well motivated in practice as data matrices often exhibit local correlations in multiple scales. Concretely, we propose a multiscale low rank modeling that represents a data matrix as a sum of block-wise low ra... View full abstract»

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  • Flexible Multilayer Sparse Approximations of Matrices and Applications

    Publication Year: 2016, Page(s):688 - 700
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1129 KB) | HTML iconHTML

    The computational cost of many signal processing and machine learning techniques is often dominated by the cost of applying certain linear operators to high-dimensional vectors. This paper introduces an algorithm aimed at reducing the complexity of applying linear operators in high dimension by approximately factorizing the corresponding matrix into few sparse factors. The approach relies on recen... View full abstract»

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  • Generic Uniqueness of a Structured Matrix Factorization and Applications in Blind Source Separation

    Publication Year: 2016, Page(s):701 - 711
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (332 KB) | HTML iconHTML

    Algebraic geometry, although little explored in signal processing, provides tools that are very convenient for investigating generic properties in a wide range of applications. Generic properties are properties that hold “almost everywhere.” We present a set of conditions that are sufficient for demonstrating the generic uniqueness of a certain structured matrix factorization. This s... View full abstract»

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  • A Provably Efficient Algorithm for Separable Topic Discovery

    Publication Year: 2016, Page(s):712 - 725
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1110 KB) | HTML iconHTML

    We develop necessary and sufficient conditions and a novel provably consistent and efficient algorithm for discovering topics (latent factors) from observations (documents) that are realized from a probabilistic mixture of shared latent factors that have certain properties. Our focus is on the class of topic models in which each shared latent factor contains a novel word that is unique to that fac... View full abstract»

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  • Linearized Kernel Dictionary Learning

    Publication Year: 2016, Page(s):726 - 739
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (741 KB) | HTML iconHTML Multimedia Media

    In this paper, we present a new approach of incorporating kernels into dictionary learning. The kernel K-SVD algorithm (KKSVD), which has been introduced recently, shows an improvement in classification performance, with relation to its linear counterpart K-SVD. However, this algorithm requires the storage and handling of a very large kernel matrix, which leads to high computational cost, while al... View full abstract»

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  • Fast Robust PCA on Graphs

    Publication Year: 2016, Page(s):740 - 756
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (8147 KB) | HTML iconHTML Multimedia Media

    Mining useful clusters from high dimensional data have received significant attention of the computer vision and pattern recognition community in the recent years. Linear and nonlinear dimensionality reduction has played an important role to overcome the curse of dimensionality. However, often such methods are accompanied with three different problems: high computational complexity (usually associ... View full abstract»

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  • Tensor CP Decomposition With Structured Factor Matrices: Algorithms and Performance

    Publication Year: 2016, Page(s):757 - 769
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2927 KB) | HTML iconHTML

    The canonical polyadic decomposition (CPD) of high-order tensors, also known as Candecomp/Parafac, is very useful for representing and analyzing multidimensional data. This paper considers a CPD model having structured matrix factors, as e.g. Toeplitz, Hankel or circulant matrices, and studies its associated estimation problem. This model arises in signal processing applications such as Wiener-Ham... View full abstract»

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  • STFT Phase Retrieval: Uniqueness Guarantees and Recovery Algorithms

    Publication Year: 2016, Page(s):770 - 781
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (904 KB) | HTML iconHTML

    The problem of recovering a signal from its Fourier magnitude is of paramount importance in various fields of engineering and applied physics. Due to the absence of Fourier phase information, some form of additional information is required in order to be able to uniquely, efficiently, and robustly identify the underlying signal. Inspired by practical methods in optical imaging, we consider the pro... View full abstract»

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  • Guaranteed Blind Sparse Spikes Deconvolution via Lifting and Convex Optimization

    Publication Year: 2016, Page(s):782 - 794
    Cited by:  Papers (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (509 KB) | HTML iconHTML

    Neural recordings, returns from radars and sonars, images in astronomy and single-molecule microscopy can be modeled as a linear superposition of a small number of scaled and delayed copies of a band-limited or diffraction-limited point spread function, which is either determined by the nature or designed by the users; in other words, we observe the convolution between a point spread function and ... View full abstract»

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  • Parametric Bilinear Generalized Approximate Message Passing

    Publication Year: 2016, Page(s):795 - 808
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1466 KB) | HTML iconHTML

    We propose a scheme to estimate the parameters bi and cj of the bilinear form zm = Σi,j bizm(i,j) cj from noisy measurements {ym}mM=1, where ym and zm are related through an arbitrary likelihood function and zm(i,j) are known. Our scheme is based on generalized approximat... View full abstract»

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  • Learning-Based Compressive Subsampling

    Publication Year: 2016, Page(s):809 - 822
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3445 KB) | HTML iconHTML

    The problem of recovering a structured signal x ∈ Cp from a set of dimensionality-reduced linear measurements b = Ax arises in a variety of applications, such as medical imaging, spectroscopy, Fourier optics, and computerized tomography. Due to computational and storage complexity or physical constraints imposed by the problem, the measurement matrix A ∈ Cn×p View full abstract»

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  • Information for Authors

    Publication Year: 2016, Page(s):823 - 824
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  • IEEE Access

    Publication Year: 2016, Page(s): 825
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  • Introducing IEEE Collabratec

    Publication Year: 2016, Page(s): 826
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  • IEEE Signal Processing Society

    Publication Year: 2016, Page(s): C3
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  • Blank page

    Publication Year: 2016, Page(s): C4
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Aims & Scope

The Journal of Selected Topics in Signal Processing (J-STSP) solicits special issues on topics that cover the entire scope of the IEEE Signal Processing Society including the theory and application of filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals by digital or analog devices or techniques.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief

Shrikanth (Shri) S. Narayanan
Viterbi School of Engineering 
University of Southern California
Los Angeles, CA 90089 USA
shri@sipi.usc.edu