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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Das, Anurag Sajjadi, Seyedhooman Mortazavi, Bobak Chaspari, Theodora Paromita, Projna Ruebush, Laura Deutz, Nicolaas Gutierrez-Osuna, Ricardo Texas A&M Univ Dept Comp Sci & Engn College Stn TX 77843 USA Texas A&M Univ Ctr Translat Res Aging & Longev College Stn TX 77843 USA

ISBN: (纸本)9781728176055

Measuring dietary intake is a major challenge in the management of chronic diseases. Current methods rely on self-report measures, which are cumbersome to obtain and often unreliable. This article presents an approach to estimate dietary intake automatically by analyzing the post-prandial glucose response (PPGR) of a meal, as measured with continuous glucose monitors. In particular, we propose a sparse-coding technique that can be used to estimate the amounts of macronutrients (carbohydrates, protein, fat) in a meal from the meal's PPGR. We use Lasso regularization to represent the PPGR of a new meal as a sparse combination of PPGRs in a dictionary, then combine the sparse weights with the macronutrient amounts in the dictionary's meals to estimate the macronutrients in the new meal. We evaluate the approach on a dataset containing nine standardized meals and their corresponding PPGRs, consumed by fifteen participants. The proposed technique consistently outperforms two baseline systems based on ridge regression and nearest-neighbors, in terms of correlation and normalized root mean square error of the predictions.

关键词： Continuous glucose monitors macronutrient prediction sparse coding

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Dynamic Graph Variational sparse coding for Power Distribution Network Fault Location and Classification

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IEEE Transactions on Industrial Informatics 2025年

作者： Khodayar, Mahdi Khodayar, Mohammad E. University of Tulsa Department of Computer Science Tulsa 74104 OK United States Southern Methodist University Department of Electrical and Computer Engineering Dallas 75275 TX United States

Fault location and classification are crucial to the reliable and resilient operation of power distribution networks (PDNs). Current machine learning works cannot provide accurate and reliable results for fault detection due to the lack of generalization power, feature sparsity, and capturing the interrelation information of adjacent PDN buses. Therefore, we propose a novel generative dynamic graph variational sparse coding framework for spatiotemporal fault detection. First, the PDN is modeled as a dynamic graph where the nodes represent the load and voltage sequence components of the buses, and edges show the impedances of the PDN lines. A dynamic graph variational autoencoder (DGVAE) is developed to learn the generative spatiotemporal features of the dynamic graph. The DGVAE incorporates a new edge propagation graph convolutional network with an attention mechanism into a deep recurrent model to capture the task-relevant spatial features of the PDN considering the interrelation information of nodes and the temporal features. Finally, a dictionary learning method is devised to learn the deep sparse representation of the captured generative features, which is mapped to the fault locations and classes. The proposed framework demonstrates superior results compared to the state-of-the-art benchmarks on the modified IEEE 69-bus and IEEE 123-bus PDNs in both fault location and classification tasks. © 2005-2012 IEEE.

关键词： Fault location and classification sparse coding spatiotemporal pattern recognition variational deep learning

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Learning Locality-Constrained sparse coding for Spectral Enhancement of Multispectral Imagery

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷 1页

作者： Hong, Danfeng Wu, Xin Gao, Lianru Zhang, Bing Chanussot, Jocelyn German Aerosp Ctr DLR Remote Sensing Technol Inst IMF D-82334 Wessling Germany Univ Grenoble Alpes GIPSA Lab CNRS Grenoble INP F-38000 Grenoble France Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Beijing Key Lab Fract Signals & Syst Beijing 100081 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci Beijing 100094 Peoples R China Univ Chinese Acad Sci Coll Resources & Environm Beijing 100049 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Univ Grenoble Alpes LJK Grenoble INP INRIACNRS F-38000 Grenoble France

Owing to easy acquisition and large coverage from the space, multispectral (MS) imaging has garnered growing interest in various applications of remote sensing. However, the limited spectral information of MS data, to a great extent, leads to difficulties in classifying the materials more accurately, particularly for those classes that have very similar visual appearances. To address this issue effectively, we attempt to enhance the spectral resolution of MS imagery, enabling the identification of materials at a more precise level by the means of richer spectral information. More specifically, we propose to learn locality-constrained sparse coding (LCSC) for short, on partially overlapped hyperspectral (HS)-MS pairs (i.e., dictionary). LCSC is capable of capturing neighboring relations well by enforcing the local constraint for each pixel. Such a strategy makes it possible to better reconstruct HS products from MS images and partially overlapped HS images. Reconstruction and unmixing are explored as potential applications to assess the performance of spectral enhancement. Extensive experiments are conducted on two HS-MS data sets in comparison with several state-of-the-art baselines, which demonstrate the effectiveness of the proposed LCSC algorithm in the task of spectral enhancement.

关键词： Image reconstruction Optimization Task analysis Spatial resolution Signal resolution Image coding Hyperspectral imaging Hyperspectral (HS) locality multispectral (MS) partially remote sensing (RS) sparse coding spectral enhancement

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Model-based sparse coding beyond Gaussian independent model

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2022年 166卷 107336-107336页

作者： Xing, Xin Xie, Rui Zhong, Wenxuan Virginia Tech Dept Stat Blacksburg VA 24060 USA Univ Cent Florida Stat & Data Sci Orlando FL 32826 USA Univ Georgia Dept Stat Athens GA 30602 USA

sparse coding aims to model data vectors as sparse linear combinations of basis elements, but a majority of related studies are restricted to continuous data without spatial or temporal structure. A new model-based sparse coding (MSC) method is proposed to provide an effective and flexible framework for learning features from different data types: continuous, discrete, or categorical, and modeling different types of correlations: spatial or temporal. The specification of the sparsity level and how to adapt the estimation method to large-scale studies are also addressed. A fast EM algorithm is proposed for estimation, and its superior performance is demonstrated in simulation and multiple real applications such as image denoising, brain connectivity study, and spatial transcriptomic imaging. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Fast EM algorithm Probabilistic model sparse coding

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A weighted sparse coding model on product Grassmann manifold for video-based human gesture recognition

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PEERJ COMPUTER SCIENCE 2022年 8卷 e923页

作者： Wang, Yuping Zhang, Junfei Capital Univ Econ & Business Sch Stat Beijing Peoples R China Cent Univ Finance & Econ Sch Stat & Math Beijing Peoples R China

It is a challenging problem to classify multi-dimensional data with complex intrinsic geometry inherent, such as human gesture recognition based on videos. In particular, manifold structure is a good way to characterize intrinsic geometry of multidimensional data. The recently proposed sparse coding on Grassmann manifold shows high discriminative power in many visual classification tasks. It represents videos on Grassmann manifold using Singular Value Decomposition (SVD) of the data matrix by vectorizing each image in videos, while vectorization destroys the spatial structure of videos. To keep the spatial structure of videos, they can be represented as the form of data tensor. In this paper, we firstly represent human gesture videos on product Grassmann manifold (PGM) by Higher Order Singular Value Decomposition (HOSVD) of data tensor. Each factor manifold characterizes features of human gesture video from different perspectives and can be understood as appearance, horizontal motion and vertical motion of human gesture video respectively. We then propose a weighted sparse coding model on PGM, where weights can be understood as modeling the importance of factor manifolds. Furthermore, we propose an optimization algorithm for learning coding coefficients by embedding each factor Grassmann manifold into symmetric matrices space. Finally, we give a classification algorithm, and experimental results on three public datasets show that our method is competitive to some relevant excellent methods.

关键词： Product Grassmann manifold sparse coding Video classification Human gesture recognition

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Tiled sparse coding in Eigenspaces for Image Classification

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INTERNATIONAL JOURNAL OF NEURAL SYSTEMS 2022年第3期32卷 2250007-2250007页

作者： Arco, Juan E. Ortiz, Andres Ramirez, Javier Zhang, Yu-Dong Gorriz, Juan M. Univ Granada Dept Signal Theory Networking & Commun Granada 18010 Spain Univ Malaga Dept Commun Engn Malaga 29010 Spain Univ Leicester Sch Informat Leicester LE1 7RH Leics England Andalusian Res Inst Data Sci & Computat Intellige Granada Spain

The automation in the diagnosis of medical images is currently a challenging task. The use of Computer Aided Diagnosis (CAD) systems can be a powerful tool for clinicians, especially in situations when hospitals are overflowed. These tools are usually based on artificial intelligence (AI), a field that has been recently revolutionized by deep learning approaches. These alternatives usually obtain a large performance based on complex solutions, leading to a high computational cost and the need of having large databases. In this work, we propose a classification framework based on sparse coding. Images are first partitioned into different tiles, and a dictionary is built after applying PCA to these tiles. The original signals are then transformed as a linear combination of the elements of the dictionary. Then, they are reconstructed by iteratively deactivating the elements associated with each component. Classification is finally performed employing as features the subsequent reconstruction errors. Performance is evaluated in a real context where distinguishing between four different pathologies: control versus bacterial pneumonia versus viral pneumonia versus COVID-19. Our system differentiates between pneumonia patients and controls with an accuracy of 97.74%, whereas in the 4-class context the accuracy is 86.73%. The excellent results and the pioneering use of sparse coding in this scenario evidence that our proposal can assist clinicians when their workload is high.

关键词： Computer-aided diagnosis medical imaging machine learning deep learning sparse coding dictionary pneumonia COVID-19

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Multi-resolution dictionary-based sparse coding method for interferometric phase image denoising

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JOURNAL OF ELECTRONIC IMAGING 2022年第3期31卷 033034-033034页

作者： Hao, Hongxing Yu, Ronghuan Hu, Huaquan Liu, Boyu Space Engn Univ Sci & Technol Complex Elect Syst Simulat Lab Beijing Peoples R China

Interferometric synthetic aperture radar (InSAR) is widely used in acquisition of digital elevation data as well as the settlement of Earth surface. Researching on the processing of space-born InSAR is of great importance in improving the accuracy of sensing data. The phase denoising method based on multi-resolution dictionary sparse coding (SC) is proposed. First, noisy blocks are extracted from the complex valued interferometric image. Then, the dictionary is trained by online method, which uses a portion of elements in the training set to update the dictionary column by column. And the trained dictionary is used to sparsely represent the noisy blocks by greedy method to denoise the image patches with different resolutions. The whole image of a given resolution is formed by combining the patches in the same resolution together. Finally, the denoising results of different resolutions are merged based on hypothesis testing and the phase estimations according to each pixels are computed. The experiments prove that the proposed denoising method based on multi-resolution SC achieves higher peak signal to noise ratio than traditional SC method. (C) 2022 SPIE and IS&T

关键词： dictionary training interferometric phase denoising sparse coding synthetic aperture radar data multi-resolution dictionary

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Primary user emulation and jamming attack detection in cognitive radio via sparse coding

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2020年第1期2020卷 1-19页

作者： Furqan, Haji M. Aygul, Mehmet A. Nazzal, Mahmoud Arslan, Huseyin Istanbul Medipol Univ Sch Engn & Nat Sci TR-34810 Istanbul Turkey Univ S Florida Dept Elect Engn Tampa FL 33620 USA

Cognitive radio is an intelligent and adaptive radio that improves the utilization of the spectrum by its opportunistic sharing. However, it is inherently vulnerable to primary user emulation and jamming attacks that degrade the spectrum utilization. In this paper, an algorithm for the detection of primary user emulation and jamming attacks in cognitive radio is proposed. The proposed algorithm is based on the sparse coding of the compressed received signal over a channel-dependent dictionary. More specifically, the convergence patterns in sparse coding according to such a dictionary are used to distinguish between a spectrum hole, a legitimate primary user, and an emulator or a jammer. The process of decision-making is carried out as a machine learning-based classification operation. Extensive numerical experiments show the effectiveness of the proposed algorithm in detecting the aforementioned attacks with high success rates. This is validated in terms of the confusion matrix quality metric. Besides, the proposed algorithm is shown to be superior to energy detection-based machine learning techniques in terms of receiver operating characteristics curves and the areas under these curves.

关键词： Cognitive radio Jamming detection Machine learning Physical layer security Primary user emulation detection Residual components sparse coding Authentication Physical layer authentication

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Graph Laplacian Regularization With sparse coding for Image Restoration and Representation

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2020年第7期30卷 2000-2014页

作者： Sha, Lingdao Schonfeld, Dan Wang, Jing Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA Tempus Labs Inc Chicago IL 60654 USA Univ Illinois Dept Math Stat & Comp Sci Chicago IL 60607 USA

sparse coding is widely used in image denoising, deblurring, clustering, and classification. However, most existing approaches to sparse coding failed to consider the fact that high dimensional data naturally reside on geometrical structure of the data space. It has been shown that geometric information of the data is important for both inversion and discrimination. In this paper, we proposed a generalized framework for image restoration and representation by combining sparse coding and graph based algorithms. In image denoising and deblurring problems, an image is first decomposed into cartoon layer (piecewise-smooth contents) and texture layer (textures and sharp edges) using morphological component analysis (MCA);then optimal graph Laplacian regularizer (OGLR) algorithm and simultaneous sparse coding with Gaussian scale mixture prior (SSC-GSM) algorithm are applied to cartoon layer and texture layer, respectively;final restored image is generated by aggregating the outcomes from two algorithms. The proposed hybrid image restoration algorithm outperforms state-of-the-art image denoising algorithms, such as BM3D on natural images, measured in PSNR, and performs comparatively in image deblurring. In image clustering and classification problems, we convert our generalized framework into a novel dual graph regularized sparse coding method to transform the nonlinear data space and feature space into linear space, two efficient optimization algorithms are provided for the numerical implementation. The experimental results show that our generalized graph Laplacian and sparse coding framework performs competitively with popular denoising, deblurring, clustering, and classification methods.

关键词： Image restoration Image coding Laplace equations Clustering algorithms Image edge detection Encoding Symmetric matrices sparse coding graph Laplacian regularizer gaussian scale mixture manifold learning image denoising image deblurring image clustering image classification morphological component analysis image decomposition

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A Benchmark for sparse coding: When Group Sparsity Meets Rank Minimization

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2020年 29卷 5094-5109页

作者： Zha, Zhiyuan Yuan, Xin Wen, Bihan Zhou, Jiantao Zhang, Jiachao Zhu, Ce Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Nokia Bell Labs Murray Hill NJ 07974 USA Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Univ Macau Dept Comp & Informat Sci Taipa 999078 Macao Peoples R China Nanjing Inst Technol Kangni Mech & Elect Inst Nanjing 211167 Peoples R China

sparse coding has achieved a great success in various image processing tasks. However, a benchmark to measure the sparsity of image patch/group is missing since sparse coding is essentially an NP-hard problem. This work attempts to fill the gap from the perspective of rank minimization. We firstly design an adaptive dictionary to bridge the gap between group-based sparse coding (GSC) and rank minimization. Then, we show that under the designed dictionary, GSC and the rank minimization problems are equivalent, and therefore the sparse coefficients of each patch group can be measured by estimating the singular values of each patch group. We thus earn a benchmark to measure the sparsity of each patch group because the singular values of the original image patch groups can be easily computed by the singular value decomposition (SVD). This benchmark can be used to evaluate performance of any kind of norm minimization methods in sparse coding through analyzing their corresponding rank minimization counterparts. Towards this end, we exploit four well-known rank minimization methods to study the sparsity of each patch group and the weighted Schatten $p$ -norm minimization (WSNM) is found to be the closest one to the real singular values of each patch group. Inspired by the aforementioned equivalence regime of rank minimization and GSC, WSNM can be translated into a non-convex weighted -norm minimization problem in GSC. By using the earned benchmark in sparse coding, the weighted -norm minimization is expected to obtain better performance than the three other norm minimization methods, i.e., -norm, -norm and weighted -norm. To verify the feasibility of the proposed benchmark, we compare the weighted -norm minimization against the three aforementioned norm minimization methods in sparse coding. Experimental results on image restoration applications, namely image inpainting and image compressive sensing recovery, demonstrate that the proposed scheme is feasible and outperforms

关键词： sparse coding GSC rank minimization adaptive dictionary weighted l(p)-norm minimization image restoration compressive sensing nuclear norm.

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