Proceedings of the IEEE

Volume 106 Issue 8 • Aug. 2018

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  • Front Cover

    Publication Year: 2018, Page(s): C1
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  • Proceedings of the IEEE publication information

    Publication Year: 2018, Page(s): C2
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  • Table of Contents

    Publication Year: 2018, Page(s):1269 - 1270
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  • The Twin Arts of Writing and Revising Technical Articles [Point of View]

    Publication Year: 2018, Page(s):1271 - 1273
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  • Rethinking PCA for Modern Data Sets: Theory, Algorithms, and Applications [Scanning the Issue]

    Publication Year: 2018, Page(s):1274 - 1276
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  • PCA in High Dimensions: An Orientation

    Publication Year: 2018, Page(s):1277 - 1292
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1143 KB) | HTML iconHTML

    When the data are high dimensional, widely used multivariate statistical methods such as principal component analysis can behave in unexpected ways. In settings where the dimension of the observations is comparable to the sample size, upward bias in sample eigenvalues and inconsistency of sample eigenvectors are among the most notable phenomena that appear. These phenomena, and the limiting behavi... View full abstract»

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  • Streaming PCA and Subspace Tracking: The Missing Data Case

    Publication Year: 2018, Page(s):1293 - 1310
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (4405 KB) | HTML iconHTML

    For many modern applications in science and engineering, data are collected in a streaming fashion carrying time-varying information, and practitioners need to process them with a limited amount of memory and computational resources in a timely manner for decision making. This often is coupled with the missing data problem, such that only a small fraction of data attributes are observed. These com... View full abstract»

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  • A Selective Overview of Sparse Principal Component Analysis

    Publication Year: 2018, Page(s):1311 - 1320
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (584 KB) | HTML iconHTML

    Principal component analysis (PCA) is a widely used technique for dimension reduction, data processing, and feature extraction. The three tasks are particularly useful and important in high-dimensional data analysis and statistical learning. However, the regular PCA encounters great fundamental challenges under high dimensionality and may produce “wrong” results. As a remedy, sparse PCA (SPCA) has... View full abstract»

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  • A Review of Distributed Algorithms for Principal Component Analysis

    Publication Year: 2018, Page(s):1321 - 1340
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2942 KB) | HTML iconHTML

    Principal component analysis (PCA) is a fundamental primitive of many data analysis, array processing, and machine learning methods. In applications where extremely large arrays of data are involved, particularly in distributed data acquisition systems, distributed PCA algorithms can harness local communications and network connectivity to overcome the need of communicating and accessing the entir... View full abstract»

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  • Extension of PCA to Higher Order Data Structures: An Introduction to Tensors, Tensor Decompositions, and Tensor PCA

    Publication Year: 2018, Page(s):1341 - 1358
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2095 KB) | HTML iconHTML

    The widespread use of multisensor technology and the emergence of big data sets have brought the necessity to develop more versatile tools to represent higher order data with multiple aspects and high dimensionality. Data in the form of multidimensional arrays, also referred to as tensors, arise in a variety of applications including chemometrics, hyperspectral imaging, high-resolution videos, neu... View full abstract»

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  • Static and Dynamic Robust PCA and Matrix Completion: A Review

    Publication Year: 2018, Page(s):1359 - 1379
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2087 KB) | HTML iconHTML

    Principal component analysis (PCA) is one of the most widely used dimension reduction techniques. Robust PCA (RPCA) refers to the problem of PCA when the data may be corrupted by outliers. Recent work by Cands, Wright, Li, and Ma defined RPCA as a problem of decomposing a given data matrix into the sum of a low-rank matrix (true data) and a sparse matrix (outliers). The column space of the low-ran... View full abstract»

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  • An Overview of Robust Subspace Recovery

    Publication Year: 2018, Page(s):1380 - 1410
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2807 KB) | HTML iconHTML

    This paper will serve as an introduction to the body of work on robust subspace recovery. Robust subspace recovery involves finding an underlying low-dimensional subspace in a data set that is possibly corrupted with outliers. While this problem is easy to state, it has been difficult to develop optimal algorithms due to its underlying nonconvexity. This work emphasizes advantages and disadvantage... View full abstract»

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  • Efficient Optimization Algorithms for Robust Principal Component Analysis and Its Variants

    Publication Year: 2018, Page(s):1411 - 1426
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1313 KB) | HTML iconHTML

    Robust principal component analysis (RPCA) has drawn significant attention in the last decade due to its success in numerous application domains, ranging from bioinformatics, statistics, and machine learning to image and video processing in computer vision. RPCA and its variants such as sparse PCA and stable PCA can be formulated as optimization problems with exploitable special structures. Many s... View full abstract»

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  • On the Applications of Robust PCA in Image and Video Processing

    Publication Year: 2018, Page(s):1427 - 1457
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (7041 KB) | HTML iconHTML

    Robust principal component analysis (RPCA) via decomposition into low-rank plus sparse matrices offers a powerful framework for a large variety of applications such as image processing, video processing, and 3-D computer vision. Indeed, most of the time these applications require to detect sparse outliers from the observed imagery data that can be approximated by a low-rank matrix. Moreover, most ... View full abstract»

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  • Imperial science: victorian cable telegraphy and the making of “maxwell’s equations” [scanning our past]

    Publication Year: 2018, Page(s):1458 - 1465
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  • Future Special Issues/Special Sections of the Proceedings

    Publication Year: 2018, Page(s): 1466
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  • Learning Has No Boundaries

    Publication Year: 2018, Page(s): 1467
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  • IEEE Access

    Publication Year: 2018, Page(s): 1468
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  • Introducing IEEE Collabratec

    Publication Year: 2018, Page(s): C3
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  • Back Cover

    Publication Year: 2018, Page(s): C4
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Aims & Scope

Proceedings of the IEEE is the leading journal to provide in-depth review, survey, and tutorial coverage of the technical developments in electronics, electrical and computer engineering, and computer science.

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Meet Our Editors

Editor-in-Chief
H. Joel Trussell
North Carolina State University