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21st IEEE International Symposium on Computer-Based Medical Systems

作者： Schleif, Prank-Michael Ongyerth, Matthias Villmann, Thomas Univ Leipzig Dept Med D-04103 Leipzig Germany

ISBN: (纸本)9780769531656

Nuclear Magnetic Resonance Spectroscopy is a technique for the analysis of complex biochemical materials. Thereby the identification of known sub-patterns is important. These measurements require an accurate preprocessing and analysis to meet clinical standards. Here we present a method for an appropriate sparse encoding of NMR spectral data combined with a fuzzy classification system allowing the identification of sub-patterns including mixtures thereof. The method is evaluated in contrast to an alternative approach using simulated metabolic spectra.

关键词： data analysis nuclear magnetic resonance sparse coding

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Efficient sparse coding Using Hierarchical Riemannian Pursuit

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2021年 69卷 4069-4084页

作者： Xue, Ye Lau, Vincent K. N. Cai, Songfu Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China

sparse coding is a class of unsupervised methods for learning a sparse representation of the input data in the form of a linear combination of a dictionary and a sparse code. This learning framework has led to state-of-the-art results in various signal processing tasks. However, classical methods learn the dictionary and the sparse code based on alternating optimizations, usually without theoretical guarantees for either optimality or convergence due to the non-convexity of the problem. Recent works on sparse coding with a complete dictionary provide strong theoretical guarantees thanks to the development of non-convex optimization. However, initial non-convex approaches learned the dictionary in the sparse coding problem sequentially in an atom-by-atom manner, which led to a long execution time. More recent works have sought to directly learn the entire dictionary at once, which substantially reduces the execution time. However, the associated recovery performance is degraded with a finite number of data samples. In this paper, we propose an efficient sparse coding scheme with a two-stage optimization. The proposed scheme leverages the global and local Riemannian geometry of the two-stage optimization problem and facilitates fast implementation for superb dictionary recovery performance by a finite number of samples. We further prove that, with high probability, the proposed scheme can exactly recover any atom in the target dictionary with a finite number of samples. Experiments on both synthetic and real-world data verify the efficiency and robustness of the proposed scheme.(1)

关键词： Dictionaries Encoding Optimization Industrial Internet of Things Convergence Wireless sensor networks Geometry sparse coding dictionary learning orthogonal group efficient implementation

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Graph Regularized sparse coding for Image Representation

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2011年第5期20卷 1327-1336页

作者： Zheng, Miao Bu, Jiajun Chen, Chun Wang, Can Zhang, Lijun Qiu, Guang Cai, Deng Zhejiang Univ Zhejiang Prov Key Lab Serv Robot Coll Comp Sci Hangzhou 310027 Zhejiang Peoples R China Zhejiang Univ State Key Lab CAD&CG Coll Comp Sci Hangzhou 310058 Zhejiang Peoples R China

sparse coding has received an increasing amount of interest in recent years. It is an unsupervised learning algorithm, which finds a basis set capturing high-level semantics in the data and learns sparse coordinates in terms of the basis set. Originally applied to modeling the human visual cortex, sparse coding has been shown useful for many applications. However, most of the existing approaches to sparse coding fail to consider the geometrical structure of the data space. In many real applications, the data is more likely to reside on a low-dimensional submanifold embedded in the high-dimensional ambient space. It has been shown that the geometrical information of the data is important for discrimination. In this paper, we propose a graph based algorithm, called graph regularized sparse coding, to learn the sparse representations that explicitly take into account the local manifold structure of the data. By using graph Laplacian as a smooth operator, the obtained sparse representations vary smoothly along the geodesics of the data manifold. The extensive experimental results on image classification and clustering have demonstrated the effectiveness of our proposed algorithm.

关键词： Image classification image clustering manifold learning sparse coding

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Underdetermined Blind Source Separation Using sparse coding

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2017年第12期28卷 3102-3108页

作者： Zhen, Liangli Peng, Dezhong Yi, Zhang Xiang, Yong Chen, Peng Sichuan Univ Coll Comp Sci Machine Intelligence Lab Chengdu 610065 Sichuan Peoples R China Deakin Univ Sch Engn & Informat Technol Geelong Vic 3217 Australia

In an underdetermined mixture system with n unknown sources, it is a challenging task to separate these sources from their m observed mixture signals, where m < n. By exploiting the technique of sparse coding, we propose an effective approach to discover some 1-D subspaces from the set consisting of all the time-frequency (TF) representation vectors of observed mixture signals. We show that these 1-D subspaces are associated with TF points where only single source possesses dominant energy. By grouping the vectors in these subspaces via hierarchical clustering algorithm, we obtain the estimation of the mixing matrix. Finally, the source signals could be recovered by solving a series of least squares problems. Since the sparse coding strategy considers the linear representation relations among all the TF representation vectors of mixing signals, the proposed algorithm can provide an accurate estimation of the mixing matrix and is robust to the noises compared with the existing underdetermined blind source separation approaches. Theoretical analysis and experimental results demonstrate the effectiveness of the proposed method.

关键词： Mixing matrix identification single source detection source recovery sparse coding underdetermined blind source separation (UBSS)

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sparse coding for Improved Signal-to-Noise Ratio in MRI 21

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21st International Conference on Neural Information Processing (ICONIP)

作者： Razzaq, Fuleah A. Mohamed, Shady Bhatti, Asim Nahavandi, Saeid Deakin Univ Ctr Intelligent Syst Res Geelong Vic 3217 Australia

ISBN: (纸本)9783319126432;9783319126425

Magnetic Resonance images (MRI) do not only exhibit sparsity but their sparsity take a certain predictable shape which is common for all kinds of images. That region based localised sparsity can be used to de-noise MR images from random thermal noise. This paper present a simple framework to exploit sparsity of MR images for image de-noising. As, noise in MR images tends to change its shape based on contrast level and signal itself, the proposed method is independent of noise shape and type and it can be used in combination with other methods.

关键词： Magnetic Resonance imaging(MRI) sparse coding Signal-to Noise Ratio (SNR) Additive White Gaussian Noise (AWGN)

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sparse coding for Hyperspectral Images using Random Dictionary and Soft Thresholding

Sparse Coding for Hyperspectral Images using Random Dictiona...

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Conference on Visual Information Processing XXI

作者： Oguslu, Ender Iftekharuddin, Khan Li, Jiang Old Dominion Univ Dept Elect & Comp Engn Norfolk VA 23529 USA

ISBN: (纸本)9780819490773

Many techniques have been recently developed for classification of hyperspectral images (HSI) including support vector machines (SVMs), neural networks and graph-based methods. To achieve good performances for the classification, a good feature representation of the HSI is essential. A great deal of feature extraction algorithms have been developed such as principal component analysis (PCA) and independent component analysis (ICA). sparse coding has recently shown state-of-the-art performances in many applications including image classification. In this paper, we present a feature extraction method for HSI data motivated by a recently developed sparse coding based image representation technique. sparse coding consists of a dictionary learning step and an encoding step. In the learning step, we compared two different methods, L1-penalized sparse coding and random selection for the dictionary learning. In the encoding step, we utilized a soft threshold activation function to obtain feature representations for HSI. We applied the proposed algorithm to a HSI dataset collected at the Kennedy Space Center (KSC) and compared our results with those obtained by a recently proposed method, supervised locally linear embedding weighted k-nearest-neighbor (SLLE-WkNN) classifier. We have achieved better performances on this dataset in terms of the overall accuracy with a random dictionary. We conclude that this simple feature extraction framework might lead to more efficient HSI classification systems.

关键词： sparse coding sparse representation dictionary learning feature extraction remote sensing hyperspectral imagery hyperspectral classification

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Forward stagewise regression with multilevel memristor for sparse coding

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Journal of Semiconductors 2023年第10期44卷 105-113页

作者： Chenxu Wu Yibai Xue Han Bao Ling Yang Jiancong Li Jing Tian Shengguang Ren Yi Li Xiangshui Miao School of Integrated Circuits Huazhong University of Science and TechnologyWuhan 430074China Hubei Yangtze Memory Laboratories Wuhan 430205China

sparse coding is a prevalent method for image inpainting and feature extraction,which can repair corrupted images or improve data processing efficiency,and has numerous applications in computer vision and signal ***,sev-eral memristor-based in-memory computing systems have been proposed to enhance the efficiency of sparse coding ***,the variations and low precision of the devices will deteriorate the dictionary,causing inevitable degradation in the accuracy and reliability of the *** this work,a digital-analog hybrid memristive sparse coding system is pro-posed utilizing a multilevel Pt/Al_(2)O_(3)/AlO_(x)/W memristor,which employs the forward stagewise regression algorithm:The approxi-mate cosine distance calculation is conducted in the analog part to speed up the computation,followed by high-precision coeffi-cient updates performed in the digital *** determine that four states of the aforementioned memristor are sufficient for the processing of natural ***,through dynamic adjustment of the mapping ratio,the precision require-ment for the digit-to-analog converters can be reduced to 4 *** to the previous system,our system achieves higher image reconstruction quality of the 38 dB peak-signal-to-noise ***,in the context of image inpainting,images containing 50%missing pixels can be restored with a reconstruction error of 0.0424 root-mean-squared error.

关键词： forward stagewise regression in-memory computing memristor sparse coding

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From sparse coding Significance to Perceptual Quality: A New Approach for Image Quality Assessment

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2018年第2期27卷 879-893页

作者： Ahar, Ayyoub Barri, Adriaan Schelkens, Peter Vrije Univ Brussel Dept Elect & Informat B-1050 Brussels Belgium IMEC B-3001 Leuven Belgium

An increasing number of image processing applications require an automated quality prediction of the visual content as perceived by humans. Since, sparse coding is suggested to be an underlying strategy of the brain's neural system, it would be logical to assume that specific tasks like quality assessment also attempt to adhere to this strategy. However, existing perceptual quality predictors, often mimicking the different stages of the human visual system and deploying machine learning strategies, such as neural networks, rarely integrate the concept of sparse coding in their design. In this paper, we first investigate the validity of such assumption by performing an empirical analysis on the relation between the structural information of the scene-captured via sparseness significance- and perceptual quality. Subsequently, we propose a new approach to integrate the significance of sparse coding features in the future imagequality measure (IQM) designs. We utilize the Fourier transform as a case study, which leads to a new IQM called sparseness significance ranking measure (SSRM). This measure essentially deploys a Fourier basis for sparse coding, a ranking mechanism based upon the amplitudes of the sparse coefficients and subsequently a complex correlation metric that assesses the correspondence between the ranked coefficient amplitude profiles of the reference and the distorted image. Moreover, we introduce a new methodology, namely separation ratio analysis, to assess the prediction quality of individual features or quality predictors given a target perceptual quality. The quality predictions by the proposed SSRM show excellent compatibility with perceptual quality scores. A set of routine benchmarking experiments utilizing the LIVE and CSIQ, IVC and TID2008 databases indicates a highly competitive performance with state of the art IQMs. Moreover, it delivers this performance at a low computational cost.

关键词： Image quality assessment structural information perceptual quality predictors human visual system sparse coding Fourier analysis sparseness significance SSRM

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Noise-robust semi-supervised learning via fast sparse coding

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PATTERN RECOGNITION 2015年第2期48卷 605-612页

作者： Lu, Zhiwu Wang, Liwei Renmin Univ China Sch Informat Beijing 100872 Peoples R China Peking Univ Sch EECS Key Lab Machine Percept MOE Beijing 100871 Peoples R China

This paper presents a novel noise-robust graph-based semi-supervised learning algorithm to deal with the challenging problem of semi-supervised learning with noisy initial labels. Inspired by the successful use of sparse coding for noise reduction, we choose to give new L-1-norm formulation of Laplacian regularization for graph-based semi-supervised learning. Since our L-1-norm Laplacian regularization is explicitly defined over the eigenvectors of the normalized Laplacian matrix, we formulate graph-based semi-supervised learning as an L-1-norm linear reconstruction problem which can be efficiently solved by sparse coding. Furthermore, by working with only a small subset of eigenvectors, we develop a fast sparse coding algorithm for our L-1-norm semi-supervised learning. Finally, we evaluate the proposed algorithm in noise-robust image classification. The experimental results on several benchmark datasets demonstrate the promising performance of the proposed algorithm. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Graph-based semi-supervised learning Noise reduction Laplacian regularization sparse coding Noise-robust image classification

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Multi-Hypergraph Consistent sparse coding

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ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 2017年第6期8卷 1–25页

作者： Feng, Xiaodong Wu, Sen Zhou, Wenjun Univ Elect Sci & Technol China Sch Polit Sci & Publ Adm 2006 Xiyuan Rd Chengdu 611731 Sichuan Peoples R China Univ Sci & Technol Beijing Donlinks Sch Econ & Management 30 Xueyuan Rd Beijing 100083 Peoples R China Univ Tennessee Business Analyt & Stat 800 Andy Hotly Tower Knoxville TN 37996 USA

sparse representation has been a powerful technique for modeling high-dimensional data. As an unsupervised technique to extract sparse representations, sparse coding encodes the original data into a new sparse code space and simultaneously learns a dictionary representing high-level semantics. Existing methods have considered local manifold within high-dimensional data using graph/hypergraph Laplacian regularization, and more from the manifold could be utilized to improve the performance. In this article, we propose to further regulate the sparse coding so that the learned sparse codes can well reconstruct the hypergraph structure. In particular, we add a novel hypergraph consistency regularization term (HC) by minimizing the reconstruction error of the hypergraph incidence or weight matrix. Moreover, we extend the HC term to multi-hypergraph consistent sparse coding (MultiCSC) and automatically select the optimal manifold structure under the multi-hypergraph learning framework. We show that the optimization of MultiCSC can be solved efficiently, and that several existing sparse coding methods can fit into the general framework of MultiCSC as special cases. As a case study, hypergraph incidence consistent sparse coding is applied to perform semi-auto image tagging, demonstrating the effectiveness of hypergraph consistency regulation. We perform further experiments using MultiCSC for image clustering, which outperforms a number of baselines.

关键词： sparse coding hypergraph consistency multiple-hypergraph learning image tagging image clustering

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