IEEE Journal of Selected Topics in Signal Processing

Early Access Articles

Early Access articles are made available in advance of the final electronic or print versions. Early Access articles are peer reviewed but may not be fully edited. They are fully citable from the moment they appear in IEEE Xplore.

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Displaying Results 1 - 25 of 29
• Improving K-Subspaces via Coherence Pursuit

Publication Year: 2018, Page(s): 1
| | PDF (5926 KB)

Subspace clustering is a powerful generalization of clustering for high-dimensional data analysis, where low-rank cluster structure is leveraged for accurate inference. K-Subspaces (KSS), an alternating algorithm that mirrors K-means, is a classical approach for clustering with this model. Like K-means, KSS is highly sensitive to initialization, yet KSS has two major handicaps beyond this issue. F... View full abstract»

• Moving Object Detection through Robust Matrix Completion Augmented with Objectness

Publication Year: 2018, Page(s): 1
| | PDF (5058 KB)

We present a novel approach for unsupervised detection of moving objects with non-salient movements (e.g. rodents in their home-cage). The proposed approach starts with separating the moving object from its background by modeling the background in a computationally efficient way. The background modeling is based on the assumption that background in natural videos lies on a low-dimensional subspace... View full abstract»

• Compressed Randomized UTV Decompositions for Low-Rank Matrix Approximations

Publication Year: 2018, Page(s): 1
| | PDF (3117 KB)

Low-rank matrix approximations play a fundamental role in numerical linear algebra and signal processing applications. This paper introduces a novel rank-revealing matrix decomposition algorithm termed Compressed Randomized UTV (CoR-UTV) decomposition along with a CoR-UTV variant aided by the power method technique. CoR-UTV is primarily developed to compute an approximation to a low-rank input mat... View full abstract»

• On Geometric Analysis of Affine Sparse Subspace Clustering

Publication Year: 2018, Page(s): 1
| | PDF (3904 KB)

Sparse subspace clustering (SSC) is a state-of-the-art method for segmenting a set of data points drawn from a union of subspaces into their respective subspaces. Recent studies have established the correctness of SSC by showing that it produces subspace-preserving affinities under broad geometric conditions. In this paper, we study the correctness of a variant of SSC, which we call affine SSC (AS... View full abstract»

• Data Recovery and Subspace Clustering from Quantized and Corrupted Measurements

Publication Year: 2018, Page(s): 1
| | PDF (1315 KB)

Quantized low-rank matrix recovery estimates the original matrix from its entry-wise quantized measurements. Subspace clustering divides data points belonging to the union of subspaces (UoS) into the respective subspaces. Generalizing from both quantized matrix recovery and subspace clustering, this paper for the first time studies the problem of combined data recovery and subspace clustering base... View full abstract»

• Nonparametric Composite Hypothesis Testing in an Asymptotic Regime

Publication Year: 2018, Page(s): 1
| | PDF (452 KB)

We investigate the nonparametric, composite hypothesis testing problem for arbitrary unknown distributions in the asymptotic regime where both the sample size and the number of hypotheses grow exponentially large. Such asymptotic analysis is important in many practical problems, where the number of variations that can exist within a family of distributions can be countably infinite. We introduce t... View full abstract»

• Data Pre-Processing for Discrimination Prevention: Information-Theoretic Optimization and Analysis

Publication Year: 2018, Page(s): 1
| | PDF (4857 KB)

Non-discrimination is a recognized objective in algorithmic decision making. In this paper, we introduce a novel probabilistic formulation of data pre-processing for reducing discrimination. We propose a convex optimization for learning a data transformation with three goals: controlling group discrimination, limiting distortion in individual data samples, and preserving utility. Several theoretic... View full abstract»

• A Modulo-Based Architecture for Analog-to-Digital Conversion

Publication Year: 2018, Page(s): 1
| | PDF (490 KB)

Systems that capture and process analog signals must first acquire them through an analog-to-digital converter. While subsequent digital processing can remove statistical correlations present in the acquired data, the dynamic range of the converter is typically scaled to match that of the input analog signal. The present paper develops an approach for analog-to-digital conversion that aims at mini... View full abstract»

• Zero-Delay Rate Distortion via Filtering for Vector-Valued Gaussian Sources

Publication Year: 2018, Page(s): 1
| | PDF (4127 KB)

We deal with zero-delay source coding of a vector-valued Gauss-Markov source subject to a mean-squared error (MSE) fidelity criterion \FS{characterized by the operational zero-delay vector-valued Gaussian rate distortion function (RDF). We address this problem by considering the nonanticipative RDF (NRDF) which is a lower bound to the causal and zero-delay RDF. We recall the realization that corre... View full abstract»

• Universal Joint Image Clustering and Registration using Multivariate Information Measures

Publication Year: 2018, Page(s): 1
| | PDF (2920 KB)

We consider the problem of universal joint clustering and registration of images. Image clustering focuses on grouping similar images, while image registration refers to the task of aligning copies of an image that have been subject to rigid-body transformations such as rotations and translations. We first study registering two images using maximum mutual information and prove its asymptotic optim... View full abstract»

• Improved Target Acquisition Rates with Feedback Codes

Publication Year: 2018, Page(s): 1
| | PDF (764 KB)

This paper considers the problem of acquiring an unknown target location (among a finite number of locations) via a sequence of measurements, where each measurement consists of simultaneously probing a group of locations. The resulting observation consists of a sum of an indicator of the target's presence in the probed region, and a zero mean Gaussian noise term whose variance is a function of the... View full abstract»

• Optimal detection and error exponents for hidden semi-Markov models

Publication Year: 2018, Page(s): 1
| | PDF (1745 KB)

We study detection of random signals corrupted by noise that over time switch their values (states) between a finite set of possible values, where the switchings occur at unknown points in time. We model such signals as hidden semi-Markov signals (HSMS), which generalize classical Markov chains by introducing explicit (possibly non-geometric) distribution for the time spent in each state. Assuming... View full abstract»

• Performance Analysis of Approximate Message Passing for Distributed Compressed Sensing

Publication Year: 2018, Page(s): 1
Cited by:  Papers (1)
| | PDF (1761 KB)

Bayesian approximate message passing (BAMP) is an efficient method in compressed sensing that is nearly optimal in the minimum mean squared error (MMSE) sense. Multiple Measurement Vector (MMV)-BAMP performs joint recovery of multiple vectors with identical support and accounts for correlations in the signal of interest and in the noise. In this paper, we show how to reduce the complexity of vecto... View full abstract»

• Efficiently Computing Messages that Reveal Selected Inferences While Protecting Others

Publication Year: 2018, Page(s): 1
| | PDF (518 KB)

• Maximum entropy low-rank matrix recovery

Publication Year: 2018, Page(s): 1
| | PDF (8765 KB)

We propose in this paper a novel, information-theoretic method, called MaxEnt, for efficient data acquisition for low-rank matrix recovery. This proposed method has important applications to a wide range of problems, including image processing and text document indexing. Fundamental to our design approach is the so-called maximum entropy principle, which states that the measurement masks which max... View full abstract»

• Classification and Representation via Separable Subspaces: Performance Limits and Algorithms

Publication Year: 2018, Page(s): 1
| | PDF (2978 KB)

We study the classification performance of Kronecker-structured models in two asymptotic regimes and developed an algorithm for separable, fast and compact K-S dictionary learning for better classification and representation of multidimensional signals by exploiting the structure in the signal. First, we study the classification performance in terms of diversity order and pairwise geometry of the ... View full abstract»

• Identifiability of Kronecker-structured Dictionaries for Tensor Data

Publication Year: 2018, Page(s): 1
| | PDF (530 KB)

This paper derives sufficient conditions for local recovery of coordinate dictionaries comprising a Kronecker-structured dictionary that is used for representing Kth-order tensor data. Tensor observations are assumed to be generated from a Kronecker-structured dictionary multiplied by sparse coefficient tensors that follow the separable sparsity model. This work provides sufficient conditions on t... View full abstract»

• Hypergraph Spectral Clustering in the Weighted Stochastic Block Model

Publication Year: 2018, Page(s): 1
| | PDF (1228 KB)

Spectral clustering is a celebrated algorithm that partitions objects based on pairwise similarity information. While this approach has been successfully applied to a variety of domains, it comes with limitations. The reason is that there are many other applications in which only multi-way similarity measures are available. This motivates us to explore the multi-way measurement setting. In this wo... View full abstract»

• Community Detection with Side Information: Exact Recovery under the Stochastic Block Model

Publication Year: 2018, Page(s): 1
Cited by:  Papers (1)
| | PDF (472 KB)

The community detection problem involves making inferences about node labels in a graph based on observing the graph edges. This paper studies the effect of additional, non-graphical side information on the phase transition of exact recovery in the binary stochastic block model (SBM) with n nodes. When side information consists of noisy labels with error probability, we show that phase transition ... View full abstract»

• Top-K Rank Aggregation from M-wise Comparisons

Publication Year: 2018, Page(s): 1
| | PDF (1405 KB)

Suppose one aims to identify only the top-K among a large collection of n items provided M-wise comparison data, where a set of M items in each data sample are ranked in order of individual preference. Natural questions that arise are: (1) how one can reliably achieve the top-K rank aggregation task; and (2) how many M-wise samples one needs to achieve it. In this paper, we answer these two questi... View full abstract»

• Lower Bounds on the Bayes Risk of the Bayesian BTL Model with Applications to Comparison Graphs

Publication Year: 2018, Page(s): 1
| | PDF (468 KB) |  Media

We consider the problem of aggregating pairwise comparisons to obtain a consensus ranking order over a collection of objects. We use the popular Bradley-Terry-Luce (BTL) model which allows us to probabilistically describe pairwise comparisons between objects. In particular, we employ the Bayesian BTL model which allows for meaningful prior assumptions and to cope with situations where the number o... View full abstract»

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