IEEE Journal of Selected Topics in Signal Processing

Volume 9 Issue 4 • June 2015

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  • [Front cover]

    Publication Year: 2015, Page(s): C1
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  • IEEE Journal of Selected Topics in Signal Processing publication information

    Publication Year: 2015, Page(s): C2
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  • [Blank page]

    Publication Year: 2015, Page(s): 580
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  • Table of Contents

    Publication Year: 2015, Page(s):581 - 582
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  • Introduction to the issue on signal processing for big data

    Publication Year: 2015, Page(s):583 - 585
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  • Structured Data Fusion

    Publication Year: 2015, Page(s):586 - 600
    Cited by:  Papers (40)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2312 KB) | HTML iconHTML

    We present structured data fusion (SDF) as a framework for the rapid prototyping of knowledge discovery in one or more possibly incomplete data sets. In SDF, each data set-stored as a dense, sparse, or incomplete tensor-is factorized with a matrix or tensor decomposition. Factorizations can be coupled, or fused, with each other by indicating which factors should be shared between data sets. At the... View full abstract»

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  • Info-Greedy Sequential Adaptive Compressed Sensing

    Publication Year: 2015, Page(s):601 - 611
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1879 KB) | HTML iconHTML

    We present an information-theoretic framework for sequential adaptive compressed sensing, Info-Greedy Sensing, where measurements are chosen to maximize the extracted information conditioned on the previous measurements. We show that the widely used bisection approach is Info-Greedy for a family of k-sparse signals by connecting compressed sensing and blackbox complexity of sequential query algori... View full abstract»

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  • Designing Statistical Estimators That Balance Sample Size, Risk, and Computational Cost

    Publication Year: 2015, Page(s):612 - 624
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2527 KB) | HTML iconHTML

    This paper proposes a tradeoff between computational time, sample complexity, and statistical accuracy that applies to statistical estimators based on convex optimization. When we have a large amount of data, we can exploit excess samples to decrease statistical risk, to decrease computational cost, or to trade off between the two. We propose to achieve this tradeoff by varying the amount of smoot... View full abstract»

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  • Online Sparsifying Transform Learning—Part I: Algorithms

    Publication Year: 2015, Page(s):625 - 636
    Cited by:  Papers (15)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2337 KB) | HTML iconHTML

    Techniques exploiting the sparsity of signals in a transform domain or dictionary have been popular in signal processing. Adaptive synthesis dictionaries have been shown to be useful in applications such as signal denoising, and medical image reconstruction. More recently, the learning of sparsifying transforms for data has received interest. The sparsifying transform model allows for cheap and ex... View full abstract»

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  • Online Sparsifying Transform Learning—Part II: Convergence Analysis

    Publication Year: 2015, Page(s):637 - 646
    Cited by:  Papers (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2730 KB) | HTML iconHTML

    Sparsity-based techniques have been widely popular in signal processing applications such as compression, denoising, and compressed sensing. Recently, the learning of sparsifying transforms for data has received interest. The advantage of the transform model is that it enables cheap and exact computations. In Part I of this work, efficient methods for online learning of square sparsifying transfor... View full abstract»

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  • Online Convex Optimization in Dynamic Environments

    Publication Year: 2015, Page(s):647 - 662
    Cited by:  Papers (15)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3309 KB) | HTML iconHTML

    High-velocity streams of high-dimensional data pose significant “big data” analysis challenges across a range of applications and settings. Online learning and online convex programming play a significant role in the rapid recovery of important or anomalous information from these large datastreams. While recent advances in online learning have led to novel and rapidly converging algo... View full abstract»

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  • Rooting our Rumor Sources in Online Social Networks: The Value of Diversity From Multiple Observations

    Publication Year: 2015, Page(s):663 - 677
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (4282 KB) | HTML iconHTML

    This paper addresses the problem of rumor source detection with multiple observations, from a statistical point of view of a spreading over a network, based on the susceptible-infectious model. For tree networks, multiple independent observations can dramatically improve the detection probability. For the case of a single rumor source, we propose a unified inference framework based on the joint ru... View full abstract»

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  • Sketch and Validate for Big Data Clustering

    Publication Year: 2015, Page(s):678 - 690
    Cited by:  Papers (8)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3957 KB) | HTML iconHTML

    In response to the need for learning tools tuned to big data analytics, the present paper introduces a framework for efficient clustering of huge sets of (possibly high-dimensional) data. Building on random sampling and consensus (RANSAC) ideas pursued earlier in a different (computer vision) context for robust regression, a suite of novel dimensionality- and set-reduction algorithms is developed.... View full abstract»

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  • Latent Space Sparse and Low-Rank Subspace Clustering

    Publication Year: 2015, Page(s):691 - 701
    Cited by:  Papers (10)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3056 KB) | HTML iconHTML

    We propose three novel algorithms for simultaneous dimensionality reduction and clustering of data lying in a union of subspaces. Specifically, we describe methods that learn the projection of data and find the sparse and/or low-rank coefficients in the low-dimensional latent space. Cluster labels are then assigned by applying spectral clustering to a similarity matrix built from these representat... View full abstract»

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  • Mining the Situation: Spatiotemporal Traffic Prediction With Big Data

    Publication Year: 2015, Page(s):702 - 715
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2513 KB) | HTML iconHTML

    With the vast availability of traffic sensors from which traffic information can be derived, a lot of research effort has been devoted to developing traffic prediction techniques, which in turn improve route navigation, traffic regulation, urban area planning, etc. One key challenge in traffic prediction is how much to rely on prediction models that are constructed using historical data in real-ti... View full abstract»

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  • RELEAF: An Algorithm for Learning and Exploiting Relevance

    Publication Year: 2015, Page(s):716 - 727
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2485 KB) | HTML iconHTML

    Recommender systems, medical diagnosis, network security, etc., require on-going learning and decision-making in real time. These-and many others-represent perfect examples of the opportunities and difficulties presented by Big Data: the available information often arrives from a variety of sources and has diverse features so that learning from all the sources may be valuable but integrating what ... View full abstract»

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  • A Distributed Tracking Algorithm for Reconstruction of Graph Signals

    Publication Year: 2015, Page(s):728 - 740
    Cited by:  Papers (14)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2864 KB) | HTML iconHTML

    The rapid development of signal processing on graphs provides a new perspective for processing large-scale data associated with irregular domains. In many practical applications, it is necessary to handle massive data sets through complex networks, in which most nodes have limited computing power. Designing efficient distributed algorithms is critical for this task. This paper focuses on the distr... View full abstract»

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  • A Universal Parallel Two-Pass MDL Context Tree Compression Algorithm

    Publication Year: 2015, Page(s):741 - 748
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1432 KB) | HTML iconHTML

    Computing problems that handle large amounts of data necessitate the use of lossless data compression for efficient storage and transmission. We present a novel lossless universal data compression algorithm that uses parallel computational units to increase the throughput. The length- N input sequence is partitioned into B blocks. Processing each block independently of the other blocks can acceler... View full abstract»

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  • A Real-Time End-to-End Multilingual Speech Recognition Architecture

    Publication Year: 2015, Page(s):749 - 759
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2035 KB) | HTML iconHTML

    Automatic speech recognition (ASR) systems are used daily by millions of people worldwide to dictate messages, control devices, initiate searches or to facilitate data input in small devices. The user experience in these scenarios depends on the quality of the speech transcriptions and on the responsiveness of the system. For multilingual users, a further obstacle to natural interaction is the mon... View full abstract»

    Open Access
  • IEEE Journal of Selected Topics in Signal Processing information for authors

    Publication Year: 2015, Page(s):760 - 761
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  • Special issue on structured matrices in signal and data processing

    Publication Year: 2015, Page(s): 762
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  • IEEE membership can help you reach your personal goals

    Publication Year: 2015, Page(s): 763
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  • Expand your professional network with IEEE

    Publication Year: 2015, Page(s): 764
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  • IEEE Signal Processing Society Information

    Publication Year: 2015, Page(s): C3
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  • [Blank page - back cover]

    Publication Year: 2015, 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