# 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.

## Filter Results

Displaying Results 1 - 25 of 41
• ### Enhance Neighbor Reversibility in Subspace Learning for Image Retrieval

Publication Year: 2018, Page(s): 1
| | PDF (7368 KB)

Two images that describe similar content usually have the neighbor-reversibility (NR) correlation, i.e., each image is among the neighbors of the other one. This phenomenon can be frequently observed in image retrieval. Some previous works have successfully utilized the NR correlation to improve search accuracy. In these methods, the retrieved images that have the NR correlation with the query ima... View full abstract»

• ### A General Framework for Understanding Compressed Subspace Clustering Algorithms

Publication Year: 2018, Page(s): 1
| | PDF (1062 KB)

Subspace clustering (SC) refers to the problem of clustering unlabeled high-dimensional data into a union of low-dimensional linear subspaces. In many practical scenarios, one may only have access to the compressed data due to constraints of measurement or computation. In this paper, based on the recently proposed restricted isometric property (RIP) of Gaussian random projection for low-dimensiona... View full abstract»

• ### t-Schatten-$p$Norm for Low-Rank Tensor Recovery

Publication Year: 2018, Page(s): 1
| | PDF (14451 KB) |  Media

In this paper, we propose a new definition of tensor Schatten-$p$norm (t-Schatten-$p$norm) based on t-SVD [1], and prove that this norm has similar properties to matrix Schatten-$p$norm. More importantly, the t-Schatten-$p$norm can better approximate the$\ell_1$ View full abstract»

• ### Coded Aperture Design for Compressive Spectral Subspace Clustering

Publication Year: 2018, Page(s): 1
| | PDF (11324 KB) |  Media

Compressive spectral imaging (CSI) acquires compressed observations of a spectral scene by applying different coding patterns at each spatial location and then performing a spectral-wise integration. Relying on compressive sensing, spectral image reconstruction is achieved by using nonlinear and relatively expensive optimization-based algorithms. In the CSI literature, several works have focused o... View full abstract»

• ### Distributed Differentially-Private Algorithms for Matrix and Tensor Factorization

Publication Year: 2018, Page(s): 1
| | PDF (4059 KB)

In many signal processing and machine learning applications, datasets containing private information are held at different locations, requiring the development of distributed privacy-preserving algorithms. Tensor and matrix factorizations are key components of many processing pipelines. In the distributed setting, differentially private algorithms suffer because they introduce noise to guarantee p... View full abstract»

• ### Successive Convex Approximation Algorithms for Sparse Signal Estimation with Nonconvex Regularizations

Publication Year: 2018, Page(s): 1
| | PDF (482 KB)

In this paper, we propose a successive convex approximation framework for sparse optimization where the nonsmooth regularization function in the objective function is nonconvex and it can be written as the difference of two convex functions. The proposed framework is based on a nontrivial combination of the majorization-minimization framework and the successive convex approximation framework propo... View full abstract»

• ### Unsupervised Joint Subspace and Dictionary Learning for Enhanced Cross-Domain Person Re-identification

Publication Year: 2018, Page(s): 1
| | PDF (2060 KB)

Person re-identification (Re-ID) has drawn increasing attention from both academia and industry due to its great potentials in surveillance applications. Most existing research efforts have attempted to tackle cross-view variation in single-domain person Re-ID. However, there is still a lack of effective approaches to cross-domain person Re-ID problem. In this paper, an Unsupervised Joint Subspace... View full abstract»

• ### Evolutionary Self-Expressive Models for Subspace Clustering

Publication Year: 2018, Page(s): 1
| | PDF (4732 KB)

The problem of organizing data that evolves over time into clusters is encountered in a number of practical settings. We introduce evolutionary subspace clustering, a method whose objective is to cluster a collection of evolving data points that lie on a union of low-dimensional evolving subspaces. To learn the parsimonious representation of the data points at each time step, we propose a non-conv... View full abstract»

• ### Unsupervised Feature Extraction for Hyperspectral Imagery Using Collaboration-Competition Graph

Publication Year: 2018, Page(s): 1
| | PDF (1248 KB)

Signal processing on graph offers the ability to define relationships of high-dimensional data on graph. In this paper, an unsupervised feature extraction method using graph for hyperspectral imagery is proposed, which incorporates collaborative representation using$\ell_2$-norm regularization with locality-constrained property into graph construction, named collabora... View full abstract»

• ### Low-complexity adaptive algorithms for robust subspace tracking

Publication Year: 2018, Page(s): 1
| | PDF (5082 KB)

This paper introduces new, low-complexity, adaptive algorithms for robust subspace tracking in certain adverse scenarios of noisy data. First, an adequate weighted least-squares criterion is considered for the design of a robust subspace tracker that is most efficient in the burst noise case. Second, by using data pre-processing and robust statistics estimate, we introduce a second method that is ... View full abstract»

• ### Low Rank Matrix Recovery with Simultaneous Presence of Outliers and Sparse Corruption

Publication Year: 2018, Page(s): 1
| | PDF (1549 KB)

We study a data model in which the data matrix${\rm D} \in \mathbb{R}^{N_{1} \times N_{2}}$can be expressed as$\rm {D = L + S + C} \qquad {\text{(1)}}$, where L is a low rank matrix, S an element-wise sparse matrix and C a matrix whose non-zero columns are outlying data points. To date, robust PCA algorithms have solely considered models ... View full abstract»

• ### Subspace Estimation from Incomplete Observations: A High-Dimensional Analysis

Publication Year: 2018, Page(s): 1
| | PDF (751 KB) |  Media

We present a high-dimensional analysis of three popular algorithms, namely, Oja's method, GROUSE and PETRELS, for subspace estimation from streaming and highly incomplete observations. We show that, with proper time scaling, the time-varying principal angles between the true subspace and its estimates given by the algorithms converge weakly to deterministic processes when the ambient dimension View full abstract»

• ### SULoRA: Subspace Unmixing with Low-Rank Attribute Embedding for Hyperspectral Data Analysis

Publication Year: 2018, Page(s): 1
| | PDF (10237 KB)

To support high-level analysis of spaceborne imaging spectroscopy (hyperspectral) imagery, spectral unmixing has been gaining significance in recent years. However, from the inevitable spectral variability, caused by illumination and topography change, atmospheric effects and so on, makes it difficult to accurately estimate abundance maps in spectral unmixing. Classical unmixing methods, e.g. line... View full abstract»

• ### Turbo-Type Message Passing Algorithms for Compressed Robust Principal Component Analysis

Publication Year: 2018, Page(s): 1
| | PDF (10594 KB)

Compressed robust principal component analysis (RPCA), in which a low-rank matrix $\L$ and a sparse matrix$\S$are recovered from an underdetermined amount of noisy linear measurements of their sum$\L+\S$, arises in various applications such as face recognition and video foreground/background separation. This problem can be solved by Bayes... View full abstract»

• ### Multi-Attribute Robust Component Analysis for Facial UV Maps

Publication Year: 2018, Page(s): 1
| | PDF (10755 KB) |  Media

The collection of large scale 3D face models has led to significant progress in the field of 3D face alignment “in-thewild”, with several methods being proposed towards establishing sparse or dense 3D correspondences between a given 2D facial image and a 3D face model. Utilizing 3D face alignment improves 2D face alignment in many ways, such as alleviating issues with artefacts and warping effects... View full abstract»

• ### Recurrent Variational Autoencoders for Learning Nonlinear Generative Models in the Presence of Outliers

Publication Year: 2018, Page(s): 1
| | PDF (10624 KB)

This paper explores two useful modifications of the recent variational autoencoder (VAE), a popular deep generative modeling framework that dresses traditional autoencoders with probabilistic attire. The first involves a specially-tailored form of conditioning that allows us to simplify the VAE decoder structure while simultaneously introducing robustness to outliers. In a related vein, a second, ... View full abstract»

• ### Graph and Sparse-based Robust Nonnegative Block Value Decomposition for Clustering

Publication Year: 2018, Page(s): 1
| | PDF (1632 KB)

In this paper, we first investigate the nonnegative block value decomposition (NBVD) approach through graph based representation for clustering called G-NBVD. Then, we propose our three-step graph and sparse-based robust NBVD (GSR-NBVD) via robust NBVD (R-NBVD) framework. The robustness to outliers is obtained by converting the Frobenius norm of error function to the$\ell_{2,1}$ View full abstract»

• ### Binary Matrix Factorization via Dictionary Learning

Publication Year: 2018, Page(s): 1
| | PDF (559 KB) |  Media

Matrix factorization is a key tool in data analysis; its applications include recommender systems, correlation analysis, signal processing, among others. Binary matrices are a particular case which has received significant attention for over thirty years, especially within the field of data mining. Dictionary learning refers to a family of methods for learning overcomplete basis (also called frame... View full abstract»

• ### Deep Multimodal Subspace Clustering Networks

Publication Year: 2018, Page(s): 1
| | PDF (16736 KB)

We present convolutional neural network (CNN) based approaches for unsupervised multimodal subspace clustering. The proposed framework consists of three main stages - multimodal encoder, self-expressive layer, and multimodal decoder. The encoder takes multimodal data as input and fuses them to a latent space representation. The self-expressive layer is responsible for enforcing the self-expressive... View full abstract»

• ### PF-FELM : A Robust PCA Feature Selection for Fuzzy Extreme Learning Machine

Publication Year: 2018, Page(s): 1
| | PDF (660 KB)

Principal component analysis (PCA) is one of the crucial dimensionality reduction (DR) techniques in which the original features are transformed into lower dimensional space. Though the PCA space has orthogonal principal components (PC), it does not provide a real reduction of dimensionality in terms of the original features (variables), as all features including irrelevant and redundant features ... View full abstract»

• ### Wasserstein Stationary Subspace Analysis

Publication Year: 2018, Page(s): 1
| | PDF (1555 KB)

Learning under non-stationarity can be achieved by decomposing the data into a subspace that is stationary and a non-stationary one (stationary subspace analysis (SSA)). While SSA has been used in various applications, its robustness and computational efficiency has limits due to the difficulty in optimizing the Kullback-Leibler divergence based objective. In this paper we contribute by extending ... View full abstract»

• ### Fast and Flexible Large Graph Embedding Based on Anchors

Publication Year: 2018, Page(s): 1
| | PDF (640 KB)

Dimensionality reduction is one of the most fundamental topic in machine learning. A range of methods focus on dimensionality reduction have been proposed in various areas. Among the unsupervised dimensionality reduction methods, graph-based dimensionality reduction has begun to draw more and more attention due to its effectiveness. However, most existing graph-based methods have high computation ... View full abstract»

• ### Adaptive L1-norm Principal-Component Analysis with Online Outlier Rejection

Publication Year: 2018, Page(s): 1
| | PDF (1102 KB)

L1-norm Principal-Component Analysis (L1-PCA) is known to attain sturdy resistance against faulty points (outliers) among the processed data. However, computing the L1-PCA of large datasets, with high number of measurements and/or dimensions, may be computationally impractical; in such cases, incremental solutions could be preferred. At the same time, in many applications it is desired to track th... View full abstract»

• ### Tensor Completion From Structurally-Missing Entries by Low-TT-rankness and Fiber-wise Sparsity

Publication Year: 2018, Page(s): 1
| | PDF (9696 KB)

Most tensor completion methods assume that missing entries are randomly distributed in incomplete tensors, and the low-rank prior or its variants are used to well pose the problem. However, this could be violated in practical applications where missing entries are not only randomly but also structurally distributed. To remedy this, this paper proposes a novel tensor completion method equipped with... View full abstract»

• ### Robust Tensor Approximation with Laplacian Scale Mixture Modeling for Multiframe Image and Video Denoising

Publication Year: 2018, Page(s): 1
| | PDF (11053 KB)

Sparse and low-rank models have been widely studied in the literature of signal processing and computer vision. However, as the dimensionality of data set increases (e.g.,multispectral images, dynamic MRI images and video sequences), the optimality of vector and matrix-based data representations and modeling tools becomes questionable. Inspired by recent advances in sparse and low-rank tensor anal... 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