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article

A novel approach for fMRI data analysis based on the combination of sparse approximation and affinity propagation clustering

Tianlong Ren, Weiming Zeng, Nizhuan Wang, Lei Chen, more

Magnetic Resonance Imaging > 2014 > 32 > 6 > 736-746

Clustering analysis has been widely used to detect the functional connectivity from functional magnetic resonance imaging (fMRI) data. However, it has some limitations such as enormous computer memory requirement, and difficulty in estimating the number of clusters. In this study, in order to effectually resolve the deficiencies mentioned above, we have proposed a novel approach (SAAPC) for fMRI data...

article

Hierarchical classification of images by sparse approximation

Byung-soo Kim, Jae Young Park, Anna C. Gilbert, Silvio Savarese, more

Image and Vision Computing > 2013 > 31 > 12 > 982-991

Using image hierarchies for visual categorization has been shown to have a number of important benefits. Doing so enables a significant gain in efficiency (e.g., logarithmic with the number of categories [16,12]) or the construction of a more meaningful distance metric for image classification [17]. A critical question, however, still remains controversial: would structuring data in a hierarchical...

article

Simultaneous‐source deblending using adaptive coherence‐constrained dictionary learning and sparse approximation

E. Isaac Evinemi, Weijian Mao

Geophysical Prospecting > 69 > 4 > 745 - 760

The dictionary learning and sparse approximation method using the K‐singular value decomposition algorithm rely on the knowledge of the sparsity or noise variance as a constraint when it is used for data denoising. However, the determination of the sparsity or noise variance of seismic data can be tricky and sometimes unknown, especially in seismic field data. Thus, where the cardinality or the noise...

article

A Data Loss Recovery Technique using Compressive Sensing for Structural Health Monitoring Applications

Venkata Sainath Gupta Thadikemalla, Abhay S. Gandhi

KSCE Journal of Civil Engineering > 2018 > 22 > 12 > 5084-5093

Recent developments in Wireless Sensor Networks (WSN) benefited various fields, among them Structural Health Monitoring (SHM) is an important application of WSNs. Using WSNs provides multiple advantages such as continuous monitoring of structure, lesser installation costs, fewer human inspections. However, because of the wireless medium, hardware faults, etc., data loss is an unavoidable consequence...

article

Learning of robust spectral graph dictionaries for distributed processing

Dorina Thanou, Pascal Frossard

EURASIP Journal on Advances in Signal Processing > 2018 > 2018 > 1 > 1-17

We consider the problem of distributed representation of signals in sensor networks, where sensors exchange quantized information with their neighbors. The signals of interest are assumed to have a sparse representation with spectral graph dictionaries. We further model the spectral dictionaries as polynomials of the graph Laplacian operator. We first study the impact of the quantization noise in...

article

The Marcinkiewicz-Type Discretization Theorems

V. N. Temlyakov

Constructive Approximation > 2018 > 48 > 2 > 337-369

This paper is devoted to discretization of integral norms of functions from a given finite dimensional subspace. This problem is very important in applications, but there is no systematic study of it. We present here a new technique, which works well for discretization of the integral norm. It is a combination of probabilistic technique, based on chaining, and results on the entropy numbers in the...

article

A New Class of Fully Discrete Sparse Fourier Transforms: Faster Stable Implementations with Guarantees

Sami Merhi, Ruochuan Zhang, Mark A. Iwen, Andrew Christlieb

Journal of Fourier Analysis and Applications > 2019 > 25 > 3 > 751-784

In this paper we consider sparse Fourier transform (SFT) algorithms for approximately computing the best s-term approximation of the discrete Fourier transform (DFT) $${\hat{\mathbf {f}}}\in {{\mathbb {C}}}^N$$ f ^ ∈ C N of any given input vector $$\mathbf {f}\in {{\mathbb {C}}}^N$$ f ∈ C N in just $$\left( s \log N\right) ^{{{\mathcal {O}}}(1)}$$ s log N ...

article

Efficient projected gradient methods for cardinality constrained optimization

Fengmin Xu, Yuhong Dai, Zhihu Zhao, Zongben Xu

Science China Mathematics > 2019 > 62 > 2 > 245-268

Sparse optimization has attracted increasing attention in numerous areas such as compressed sens-ing, financial optimization and image processing. In this paper, we first consider a special class of cardinality constrained optimization problems, which involves box constraints and a singly linear constraint. An effcient approach is provided for calculating the projection over the feasibility set after...

article

Reconstruction of sparse-view tomography via preconditioned Radon sensing matrix

Prasad Theeda, P. U. Praveen Kumar, C. S. Sastry, P. V. Jampana

Journal of Applied Mathematics and Computing > 2019 > 59 > 1-2 > 285-303

Computed Tomography (CT) is one of the significant research areas in the field of medical image analysis. As X-rays used in CT image reconstruction are harmful to the human body, it is necessary to reduce the X-ray dosage while also maintaining good quality of CT images. Since medical images have a natural sparsity, one can directly employ compressive sensing (CS) techniques to reconstruct the CT...

article

Gaussian field consensus: A robust nonparametric matching method for outlier rejection

Gang Wang, Yufei Chen, Xiangwei Zheng

Pattern Recognition > 2018 > 74 > C > 305-316

In this paper, we propose a robust method, called Gaussian Field Consensus (GFC), for outlier rejection from given putative point set matching correspondences. Finding correct correspondences (inliers) is a key component in many computer vision and pattern recognition tasks, and the goal of outlier (mismatch) rejection is to fit the transformation function that maps one feature point set to another...

article

IRG2016: RBF-based regional geoid model of Iran

Abdoreza Saadat, Abdolreza Safari, Deanna Needell

Studia Geophysica et Geodaetica > 2018 > 62 > 3 > 380-407

The aim of this research is the optimal determination of the regional geoid model of Iran based on radial basis functions (RBFs). In this case, the type and number of RBFs, their horizontal positions, depths, and unknown coefficients must be properly determined. The quality of calculations strongly depends on the correct choice of these unknown parameters. Given the precise geocentric position of...

article

A generic top-level mechanism for accelerating signal recovery in compressed sensing

Ahmad M. Rateb, Sharifah-Kamilah Syed-Yusof, Rozeha A. Rashid

Digital Signal Processing > 2017 > 69 > C > 237-251

Compressed Sensing (CS) is challenged by the computational complexity associated with signal recovery, especially in applications that involve recovering high-dimensional signals. We present an efficient mechanism based on divide-and-conquer principle for accelerating CS signal recovery with minimal impact on recovery quality. In principle, the proposed mechanism is applicable to any CS recovery algorithm,...

article

Multilevel and Multi-index Monte Carlo methods for the McKean–Vlasov equation

Abdul-Lateef Haji-Ali, Raúl Tempone

Statistics and Computing > 2018 > 28 > 4 > 923-935

We address the approximation of functionals depending on a system of particles, described by stochastic differential equations (SDEs), in the mean-field limit when the number of particles approaches infinity. This problem is equivalent to estimating the weak solution of the limiting McKean–Vlasov SDE. To that end, our approach uses systems with finite numbers of particles and a time-stepping scheme...

article

Sparse Regularization via Convex Analysis

Ivan Selesnick

IEEE Transactions on Signal Processing > 2017 > 65 > 17 > 4481 - 4494

Sparse approximate solutions to linear equations are classically obtained via L1 norm regularized least squares, but this method often underestimates the true solution. As an alternative to the L1 norm, this paper proposes a class of nonconvex penalty functions that maintain the convexity of the least squares cost function to be minimized, and avoids the systematic underestimation characteristic of...

article

Optimizing the cervix cytological examination based on deep learning and dynamic shape modeling

Afaf Tareef, Yang Song, Heng Huang, Yue Wang, more

Neurocomputing > 2017 > 248 > C > 28-40

The task of segmenting nuclei and cytoplasm in Papanicolau smear images is one of the most challenging tasks in automated cervix cytological analysis owing to the high degree of overlapping, the multiform shape of the cells and their complex structures resulting from inconsistent staining, poor contrast, and the presence of inflammatory cells. This article presents a robust variational segmentation...

article

Sparsity Based Efficient Cross-Correlation Techniques in Sensor Networks

Prasant Misra, Wen Hu, Mingrui Yang, Marco Duarte, more

IEEE Transactions on Mobile Computing > 2017 > 16 > 7 > 2037 - 2050

Cross-correlation is a popular signal processing technique used in numerous location tracking systems for obtaining reliable range information. However, its efficient design and practical implementation has not yet been achieved on mote platforms that are typical in wireless sensor network due to resource constrains. In this paper, we propose StructS-XCorr: cross-correlation via structured sparse...

article

Robustness properties of dimensionality reduction with Gaussian random matrices

Bin Han, ZhiQiang Xu

Science China Mathematics > 2017 > 60 > 10 > 1753-1778

In this paper, motivated by the results in compressive phase retrieval, we study the robustness properties of dimensionality reduction with Gaussian random matrices having arbitrarily erased rows. We first study the robustness property against erasure for the almost norm preservation property of Gaussian random matrices by obtaining the optimal estimate of the erasure ratio for a small given norm...

article

Sparse Signal Approximation via Nonseparable Regularization

Ivan Selesnick, Masoud Farshchian

IEEE Transactions on Signal Processing > 2017 > 65 > 10 > 2561 - 2575

The calculation of a sparse approximate solution to a linear system of equations is often performed using either L1-norm regularization and convex optimization or nonconvex regularization and nonconvex optimization. Combining these principles, this paper describes a type of nonconvex regularization that maintains the convexity of the objective function, thereby allowing the calculation of a sparse...

chapter

Parallel computations for real-time implementation of adaptive sparse approximation methods

A. A. Kim, O. O. Lukovenkova, Yu. V. Marapulets, A. B. Tristanov

2017 XX IEEE International Conference on Soft Computing and Measurements (SCM) > 301 - 303

2017 XX IEEE International Conference on Soft Computing and Measurements (SCM)

The article is devoted to a new approach to the analysis and modeling of short geoacoustic signals. A parallel implementation of the applied methods is described.

chapter

Dictionary learning with residual codes

Yigit Oktar, Mehmet Turkan

2017 25th Signal Processing and Communications Applications Conference (SIU) > 1 - 4

2017 25th Signal Processing and Communications Applications Conference (SIU)

In conventional sparse representations based dictionary learning algorithms, initial dictionaries are generally assumed to be proper representatives of the system at hand. However, this may not be the case, especially in some systems restricted to random initialization. Therefore, a supposedly optimal state-update based on such an improper model might lead to undesired effects that will be conveyed...

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Search results

A novel approach for fMRI data analysis based on the combination of sparse approximation and affinity propagation clustering

Hierarchical classification of images by sparse approximation

Simultaneous‐source deblending using adaptive coherence‐constrained dictionary learning and sparse approximation

A Data Loss Recovery Technique using Compressive Sensing for Structural Health Monitoring Applications

Learning of robust spectral graph dictionaries for distributed processing

The Marcinkiewicz-Type Discretization Theorems

A New Class of Fully Discrete Sparse Fourier Transforms: Faster Stable Implementations with Guarantees

Efficient projected gradient methods for cardinality constrained optimization

Reconstruction of sparse-view tomography via preconditioned Radon sensing matrix

Gaussian field consensus: A robust nonparametric matching method for outlier rejection

IRG2016: RBF-based regional geoid model of Iran

A generic top-level mechanism for accelerating signal recovery in compressed sensing

Multilevel and Multi-index Monte Carlo methods for the McKean–Vlasov equation

Sparse Regularization via Convex Analysis

Optimizing the cervix cytological examination based on deep learning and dynamic shape modeling

Sparsity Based Efficient Cross-Correlation Techniques in Sensor Networks

Robustness properties of dimensionality reduction with Gaussian random matrices

Sparse Signal Approximation via Nonseparable Regularization

Parallel computations for real-time implementation of adaptive sparse approximation methods

Dictionary learning with residual codes

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