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An Accurate and Efficient Device-Free Localization Approach Based on sparse coding in Subspace

IEEE ACCESS 2018年 6卷 61782-61799页

作者： Huang, Huakun Zhao, Haoli Li, Xiang Ding, Shuxue Zhao, Lingjun Li, Zhenni Univ Aizu Sch Comp Sci & Engn Aizu Wakamatsu Fukushima 9650005 Japan Guangdong Univ Technol Sch Automat Guangdong Key Lab IoT Informat Technol Guangzhou 510006 Guangdong Peoples R China

In practical device-free localization (DFL) applications, for enlarging the monitoring area and improving localization accuracy, too many nodes need to be deployed, which results in a large volume of DFL data with high dimensions. This arises a key problem of seeking an accurate and efficient approach for DFL. In order to address this problem, this paper regards DFL as a problem of sparse-representation-based classification;builds a sparse model;and then proposes two sparse-coding-based algorithms. The first algorithm, sparse coding via the iterative shrinkage-thresholding algorithm (SC-ISTA), is efficient for handling high-dimensional data. And then, subspace techniques are further utilized, followed by performing sparse coding in the low-dimensional signal subspace, which leads to the second algorithm termed subspace-based SC-ISTA (SSC-ISTA). Experiments with the real-world data set are conducted for single-target and multi-target localization, and three typical machine learning algorithms, deep learning based on autoencoder, K-nearest neighbor, and orthogonal matching pursuit, are compared. Experimental results show that both SC-ISTA and SSC-ISTA can achieve high localization accuracies of 100% and are robust to noisy data when SNR is greater than 10 dB, and the time costs for sparse coding of SC-ISTA and SSC-ISTA are 2.1 x 10(-3) s and 2.1 x 10(-4) s respectively, which indicates that the proposed algorithms outperform the other three ones.

关键词： Device-free localization wireless sensor networks multi-targets sparse coding subspace iterative shrinkage-thresholding algorithm

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Multi-information fusion sparse coding with preserving local structure for hyperspectral image classification

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JOURNAL OF APPLIED REMOTE SENSING 2017年第4期11卷

作者： Wei, Xiaohui Zhu, Wen Liao, Bo Gu, Changlong Li, Weibiao Hunan Univ Coll Informat Sci & Engn Changsha Hunan Peoples R China

The key question of sparse coding (SC) is how to exploit the information that already exists to acquire the robust sparse representations (SRs) of distinguishing different objects for hyperspectral image (HSI) classification. We propose a multi-information fusion SC framework, which fuses the spectral, spatial, and label information in the same level, to solve the above question. In particular, pixels from disjointed spatial clusters, which are obtained by cutting the given HSI in space, are individually and sparsely encoded. Then, due to the importance of spatial structure, graph-and hypergraph-based regularizers are enforced to motivate the obtained representations smoothness and to preserve the local consistency for each spatial cluster. The latter simultaneously considers the spectrum, spatial, and label information of multiple pixels that have a great probability with the same label. Finally, a linear support vector machine is selected as the final classifier with the learned SRs as input. Experiments conducted on three frequently used real HSIs show that our methods can achieve satisfactory results compared with other state-of-the-art methods. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： hyperspectral image classification multi-information fusion sparse coding hyper-Laplacian parallel sparse recovery support vector machine

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Robust Joint Graph sparse coding for Unsupervised Spectral Feature Selection

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2017年第6期28卷 1263-1275页

作者： Zhu, Xiaofeng Li, Xuelong Zhang, Shichao Ju, Chunhua Wu, Xindong Guangxi Normal Univ Guangxi Key Lab Multisource Informat Min & Secur Guilin 541004 Peoples R China Chinese Acad Sci Xian Inst Opt & Precis Mech State Key Lab Transient Opt & Photon Ctr OPT IMagery Anal & Learning OPTIMAL Xian 710119 Shaanxi Peoples R China Zhejiang Gongshang Univ Sch Comp Sci & Informat Technol Hangzhou 310018 Zhejiang Peoples R China Univ Vermont Dept Comp Sci Burlington VT 05405 USA

In this paper, we propose a new unsupervised spectral feature selection model by embedding a graph regularizer into the framework of joint sparse regression for preserving the local structures of data. To do this, we first extract the bases of training data by previous dictionary learning methods and, then, map original data into the basis space to generate their new representations, by proposing a novel joint graph sparse coding (JGSC) model. In JGSC, we first formulate its objective function by simultaneously taking subspace learning and joint sparse regression into account, then, design a new optimization solution to solve the resulting objective function, and further prove the convergence of the proposed solution. Furthermore, we extend JGSC to a robust JGSC (RJGSC) via replacing the least square loss function with a robust loss function, for achieving the same goals and also avoiding the impact of outliers. Finally, experimental results on real data sets showed that both JGSC and RJGSC outperformed the state-of-the-art algorithms in terms of k-nearest neighbor classification performance.

关键词： Dimensionality reduction manifold learning regression sparse coding

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Maximum-margin sparse coding

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NEUROCOMPUTING 2017年 238卷 340-350页

作者： Liu, Chien-Liang Hsaio, Wen-Hoar Xiao, Bin Chen, Chun-Yu Wu, Wei-Liang Natl Chiao Tung Univ Dept Ind Engn & Management Hsinchu Taiwan Natl Chung Shan Inst Sci & Technol Informat Management Ctr Taoyuan Taiwan Natl Chiao Tung Univ Dept Comp Sci Hsinchu Taiwan

This work devises a maximum-margin sparse coding algorithm, jointly considering reconstruction loss and hinge loss in the model. The sparse representation along with maximum-margin constraint is analogous to kernel trick and maximum-margin properties of support vector machine (SVM), giving a base for the proposed algorithm to perform well in classification tasks. The key idea behind the proposed method is to use labeled and unlabeled data to learn discriminative representations and model parameters simultaneously, making it easier to classify data in the new space. We propose to use block coordinate descent to learn all the components of the proposed model and give detailed derivation for the update rules of the model variables. Theoretical analysis on the convergence of the proposed MMSC algorithm is provided based on Zangwill's global convergence theorem. Additionally, most previous research studies on dictionary learning suggest to use an overcomplete dictionary to improve classification performance, but it is computationally intensive when the dimension of the input data is huge. We conduct experiments on several real data sets, including Extended YaleB, AR face, and Caltech101 data sets. The experimental results indicate that the proposed algorithm outperforms other comparison algorithms without an over-complete dictionary, providing flexibility to deal with high-dimensional data sets. (C)2017 Elsevier B.V. All rights reserved.

关键词： Maximum-margin sparse coding Block coordinate descent

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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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Speech enhancement with a GSC-like structure employing sparse coding

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Journal of Zhejiang University-Science C(Computers and Electronics) 2014年第12期15卷 1154-1163页

作者： Li-chun YANG Yun-tao QIAN College of Computer Science and Technology Zhejiang University Intelligent Control Research Institute Zhejiang Wanli University

Speech communication is often influenced by various types of interfering signals. To improve the quality of the desired signal, a generalized sidelobe canceller(GSC), which uses a reference signal to estimate the interfering signal, is attracting attention of researchers. However, the interference suppression of GSC is limited since a little residual desired signal leaks into the reference signal. To overcome this problem, we use sparse coding to suppress the residual desired signal while preserving the reference signal. sparse coding with the learned dictionary is usually used to reconstruct the desired signal. As the training samples of a desired signal for dictionary learning are not observable in the real environment, the reconstructed desired signal may contain a lot of residual interfering signal. In contrast,the training samples of the interfering signal during the absence of the desired signal for interferer dictionary learning can be achieved through voice activity detection(VAD). Since the reference signal of an interfering signal is coherent to the interferer dictionary, it can be well restructured by sparse coding, while the residual desired signal will be removed. The performance of GSC will be improved since the estimate of the interfering signal with the proposed reference signal is more accurate than ever. Simulation and experiments on a real acoustic environment show that our proposed method is effective in suppressing interfering signals.

关键词： Generalized sidelobe canceller Speech enhancement Voice activity detection Dictionary learning sparse coding

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Orthogonal Projective sparse coding for image representation

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NEUROCOMPUTING 2016年 173卷 270-277页

作者： Zhao, Wei Liu, Zheng Guan, Ziyu Lin, Binbin Cai, Deng Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Shaanxi Peoples R China Northwest Univ China Coll Informat & Technol Xian 710127 Shaanxi Peoples R China Zhejiang Univ State Key Lab CAD & CG Hangzhou 310058 Zhejiang Peoples R China

We consider the problem of image representation for visual analysis. When representing images as vectors, the feature space is of very high dimensionality, which makes it difficult for applying statistical techniques for visual analysis. One then hope to apply matrix factorization techniques, such as Singular Vector Decomposition (SVD) to learn the low dimensional hidden concept space. Among various matrix factorization techniques, sparse coding receives considerable interests in recent years because its sparse representation leads to an elegant interpretation. However, most of the existing sparse coding algorithms are computational expensive since they compute the basis vectors and the representations iteratively. In this paper, we propose a novel method, called Orthogonal Projective sparse coding (OPSC), for efficient and effective image representation and analysis. Integrating the techniques from manifold learning and sparse coding, OPSC provides a sparse representation which can capture the intrinsic geometric structure of the image space. Extensive experimental results on real world applications demonstrate the effectiveness and efficiency of the proposed approach. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Manifold sparse coding Orthogonal projection

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sparse coding approaches for neuron spike sorting

Sparse coding approaches for neuron spike sorting

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2016 IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference(IMCEC 2016)

作者： Kai Yang Haifeng Wu Yu Zeng School of Electrical and Information Technology Yunnan Minzu University Key Laboratory of Wireless Sensor Networks of Yunnan Province

ISBN: (纸本)9781509001668

Spike sorting is in fact clustering analysis,which will face a problem in high dimensions: the "curse of dimensionality".This paper proposes a new dimension-reduction approach for sorting spikes from multiunit *** new approach applies the principle of sparse coding to analyzing high-dimensional spike data within the frameworks of clustering,classification and dimension ***,the sparse representation adopts fast parallel active-set optimization to speed up the convergence of the *** our experiment,we compare the proposed approach with traditional *** is shown that our approach has fewer extracted features and better classification accuracy especially in similar spike waveforms.

关键词： Spike sorting Principal Components Analysis sparse coding

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Efficient Hardware Architecture for sparse coding

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2014年第16期62卷 4173-4186页

作者： Kim, Jung Kuk Knag, Phil Chen, Thomas Zhang, Zhengya Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA

sparse coding encodes natural stimuli using a small number of basis functions known as receptive fields. In this work, we design custom hardware architectures for efficient and high-performance implementations of a sparse coding algorithm called the sparse and independent local network (SAILnet). A study of the neuron spiking dynamics uncovers important design considerations involving the neural network size, target firing rate, and neuron update step size. Optimal tuning of these parameters keeps the neuron spikes sparse and random to achieve the best image fidelity. We investigate practical hardware architectures for SAILnet: a bus architecture that provides efficient neuron communications, but results in spike collisions;and a ring architecture that is more scalable, but causes neuron misfires. We show that the spike collision rate is reduced with a sparse spiking neural network, so an arbitration-free bus architecture can be designed to tolerate collisions without the need of arbitration. To reduce neuron misfires, we design a latent ring architecture to damp the neuron responses for an improved image fidelity. The bus and the ring architecture can be combined in a hybrid architecture to achieve both high throughput and scalability. The three architectures are synthesized and place-and-routed in a 65 nm CMOS technology. The proof-of-concept designs demonstrate a high sparse coding throughput up to 952 M pixels per second at an energy consumption of 0.486 nJ per pixel.

关键词： Algorithm and architecture co-optimization hardware acceleration neural network architecture sparse and independent local network sparse coding

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Medical image registration using sparse coding of image patches

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COMPUTERS IN BIOLOGY AND MEDICINE 2016年 73卷 56-70页

作者： Afzali, Maryam Ghaffari, Aboozar Fatemizadeh, Emad Soltanian-Zadeh, Hamid Sharif Univ Technol Biomed Signal & Image Proc Lab BiSIPL Dept Elect Engn Tehran Iran Univ Tehran Sch Elect & Comp Engn CIPCE Tehran Iran Inst Res Fundamental Sci IPM Sch Cognit Sci Tehran Iran Henry Ford Hlth Syst Dept Radiol Image Anal Lab Detroit MI USA Henry Ford Hlth Syst Dept Res Adm Image Anal Lab Detroit MI USA

Image registration is a basic task in medical image processing applications like group analysis and atlas construction. Similarity measure is a critical ingredient of image registration. Intensity distortion of medical images is not considered in most previous similarity measures. Therefore, in the presence of bias field distortions, they do not generate an acceptable registration. In this paper, we propose a sparse based similarity measure for mono-modal images that considers non-stationary intensity and spatially-varying distortions: The main idea behind this measure is that the aligned image is constructed by an analysis dictionary trained using the image patches. For this purpose, we use "Analysis K-SVD" to train the dictionary and find the sparse coefficients. We utilize image patches to construct the analysis dictionary and then we employ the proposed sparse similarity measure to find a non-rigid transformation using free form deformation (FFD). Experimental results show that the proposed approach is able to robustly register 2D and 3D images in both simulated and real cases. The proposed method outperforms other state-of-the-art similarity measures and decreases the transformation error compared to the previous methods. Even in the presence of bias field distortion, the proposed method aligns images without any preprocessing. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Non-rigid image registration Analysis K-SVD algorithm sparse coding

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