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Transfer robust sparse coding based on graph and joint distribution adaption for image representation

KNOWLEDGE-BASED SYSTEMS 2018年 147卷 1-11页

作者： Zhao, Peng Wang, Wei Lu, Yijuan Liu, Huiting Yao, Sheng Anhui Univ Minist Educ Key Lab Intelligent Comp & Signal Proc Hefei 230039 Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Anhui Peoples R China Texas State Univ Dept Comp Sci San Marcos TX 78666 USA

Transfer learning can transfer knowledge from a source domain to a target domain, promoting the performance of the model learned from the source data. sparse coding can make the representation of a model more succinct and easy to manipulate. Existing transfer sparse coding methods assume the data from the source and the target domains are accurate, which can provide useful information. However, in many real applications, the data in the source and target domains may contain noise and useless information, which could severely degrade the performance of the learned model. In this paper, we propose a transfer robust sparse coding based on graph and joint distribution adaption for image representation. The noise matrix model is utilized to handle noise and useless information in the transfer sparse coding. Moreover, the differences of marginal distribution and conditional distribution are simultaneously reduced in the transfer robust sparse coding. Extensive experiments on six benchmark datasets show the proposed method can effectively deal with the noise and useless information and therefore outperforms several state-of-the-art transfer learning methods on cross-distribution domains. (c) 2018Elsevier B.V. All rights reserved.

关键词： Transfer learning sparse coding Image representation

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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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Iterative Semi-Supervised sparse coding Model for Image Classification

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JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY 2015年第1期81卷 99-110页

作者： Zheng, Haixia Ip, Horace H. S. City Univ Hong Kong Dept Comp Sci Ctr Innovat Applicat Internet & Multimedia Techno AIMtech Ctr Kowloon Hong Kong Peoples R China

The scarcity of labeled data and the high-dimensionality of multimedia data are the major obstacles for image classification. Due to these concerns, this paper proposes a novel algorithm, Iterative Semi-supervised sparse coding (ISSC), which jointly explores the advantages of both sparse coding and graph-based semi-supervised learning in order to learn discriminative sparse codes as well as an effective classification function. The ISSC algorithm fully exploits initial labels and the subsequently predicted labels for sparse codes learning. At the same time, during the graph-based semi-supervised learning stage, similarity matrix is firstly adjusted through the latest learned sparse codes, and then is utilized to obtain a better classification function. To make the ISSC scale up to larger databases, a novel online dictionary learning algorithm is also proposed to update the dictionary incrementally. In particular, by solving quadratic optimization, the ISSC approach can give rise to closed-form solutions for sparse codes and classification function, respectively. It has been extensively evaluated over three widely used datasets for image classification task. The experimental results in terms of classification accuracy demonstrate the proposed ISSC approach can achieve significant performance improvements with respect to the state-of-the-arts.

关键词： Image classification sparse coding Graph-based semi-supervised learning Incremental dictionary learning

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Spectrum enhancement with sparse coding for robust speech recognition

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DIGITAL SIGNAL PROCESSING 2015年 43卷 59-70页

作者： He, Yongjun Sun, Guanglu Han, Jiqing Harbin Univ Sci & Technol Harbin 150080 Peoples R China Harbin Inst Technol Harbin 150001 Peoples R China

Recently, a trend in speech recognition is to introduce sparse coding for noise robustness. Although several methods have been proposed, the performance of sparse coding in speech denoising is not so optimistic. One assumption with sparse coding is that the representation of speech over the speech dictionary is sparse, while that of the noise is dense. This assumption is obviously not sustained in the speech denoising scenario. Many noises are also sparse over the speech dictionary. In such a condition, the representation of noisy speech still contains noise components, resulting in degraded performance. To solve this problem, we first analyze the assumption of sparse coding and then propose a novel method to enhance speech spectrum. This method first finds out the atoms which represent the noise sparsely, and then selectively ignores them in the reconstruction of speech to reduce the residual noise. Speech features are then extracted from the enhanced spectrum for speech recognition. Experimental results show that the proposed method can improve the noise robustness of a speech recognition system substantially. (C) 2015 Elsevier Inc. All rights reserved.

关键词： sparse coding Speech denoising Residual noise Basis pursuit denoising

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Statistical method for sparse coding of speech including a linear predictive model

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2006年第367期367卷 231-251页

作者： Rufiner, Hugo L. Goddard, John Rocha, Luis F. Torres, Maria E. Univ Nac Entre Rios Fac Ingn RA-3100 Oro Verde Entre Rios Argentina Univ Autonoma Metropolitana Iztapalapa Dept Ing Elect Mexico City DF Mexico Univ Buenos Aires Fac Ingn RA-1053 Buenos Aires DF Argentina

Recently, different methods for obtaining sparse representations of a signal using dictionaries of waveforms have been studied. They are often motivated by the way the brain seems to process certain sensory signals. Algorithms have been developed using a specific criterion to choose the waveforms occurring in the representation. The waveforms are choosen from a fixed dictionary and some algorithms also construct them as a part of the method. In the case of speech signals, most approaches do not take into consideration the important temporal correlations that are exhibited. It is known that these correlations are well approximated by linear models. Incorporating this a priori knowledge of the signal can facilitate the search for a suitable representation solution and also can help with its interpretation. Lewicki proposed a method to solve the noisy and overcomplete independent component analysis problem. In the present paper we propose a modification of this statistical technique for obtaining a sparse representation using a generative parametric model. The representations obtained with the method proposed here and other techniques are applied to artificial data and real speech signals, and compared using different coding costs and sparsity measures. The results show that the proposed method achieves more efficient representations of these signals compared to the others. A qualitative analysis of these results is also presented, which suggests that the restriction imposed by the parametric model is helpful in discovering meaningful characteristics of the signals. (c) 2005 Elsevier B.V. All rights reserved.

关键词： speech analysis and representation sparse coding linear predictive coding basis pursuit matching pursuit independent component analysis

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Fast Face Recognition Via sparse coding and Extreme Learning Machine

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COGNITIVE COMPUTATION 2014年第2期6卷 264-277页

作者： He, Bo Xu, Dongxun Nian, Rui van Heeswijk, Mark Yu, Qi Miche, Yoan Lendasse, Amaury Ocean Univ China Sch Informat Sci & Engn Qingdao 266100 Shandong Peoples R China Aalto Univ Dept Informat & Comp Sci Aalto 00076 Finland Basque Fdn Sci IKERBASQUE Bilbao 48011 Spain Univ Basque Country Fac Comp Sci Computat Intelligence Grp Donostia San Sebastian Spain

Most face recognition approaches developed so far regard the sparse coding as one of the essential means, while the sparse coding models have been hampered by the extremely expensive computational cost in the implementation. In this paper, a novel scheme for the fast face recognition is presented via extreme learning machine (ELM) and sparse coding. The common feature hypothesis is first introduced to extract the basis function from the local universal images, and then the single hidden layer feedforward network (SLFN) is established to simulate the sparse coding process for the face images by ELM algorithm. Some developments have been done to maintain the efficient inherent information embedding in the ELM learning. The resulting local sparse coding coefficient will then be grouped into the global representation and further fed into the ELM ensemble which is composed of a number of SLFNs for face recognition. The simulation results have shown the good performance in the proposed approach that could be comparable to the state-of-the-art techniques at a much higher speed.

关键词： Extreme learning machine Common feature hypothesis sparse coding Face recognition

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An associative sparse coding neural network and applications

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NEUROCOMPUTING 2010年第4-6期73卷 684-689页

作者： Zeng, Xianhua Luo, Siwei Li, Qingyong Chongqing Univ Posts & Telecommun Coll Comp Sci & Technol Chongqing 400065 Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China

Some linear sparse coding models have been proposed for modeling responses in the early stage of the visual system, but nonlinear operations are ubiquitous in visual cortex. So we put forward an associative sparse coding neural network (ASCNN) with nonlinear response property in top-layer coding units. In our ASCNN model, the choices of sparseness function must correspond to the characters of activation function. This paper gives several reasonable and efficient methods for constructing sparseness functions and activation functions. Experiment on benchmark natural image dataset shows that our model can successfully simulate receptive field and nonlinear sparse response property of simple cells. Moreover, in two recognition tasks on face images and handwritten digits, experimental results show that our model works much better than linear sparse coding model (sparsenet) by combining with linear neural network classifier. (C) 2009 Elsevier B.V. Ali rights reserved.

关键词： Neural network Nonlinearity Receptive field sparse coding Recognition

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An adaptive bimodal recognition framework using sparse coding for face and ear

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PATTERN RECOGNITION LETTERS 2015年 53卷 69-76页

作者： Huang, Zengxi Liu, Yiguang Li, Xuwei Li, Jie Xihua Univ Sch Math & Comp Engn Chengdu 610039 Peoples R China Sichuan Univ Coll Comp Sci Vis & Image Proc Lab Chengdu 610065 Peoples R China

In this paper, we propose an adaptive face and ear based bimodal recognition framework using sparse coding, namely ABSRC, which can effectively reduce the adverse effect of degraded modality. A unified and reliable biometric quality measure based on sparse coding is presented for both face and ear, which relies On the collaborative representation by all classes. For adaptive feature fusion, a flexible piecewise function is carefully designed to select feature weights based on their qualities. ABSRC utilizes a two-phase sparse coding strategy. At first, face and ear features are separately coded on their associated dictionaries for individual quality assessments. Secondly, the weighted features are concatenated to form a unique feature vector, which is then coded and classified in multimodal feature space. Experiments demonstrate that ABSRC achieves quite encouraging robustness against image degeneration, and outperforms many up-to-date methods. Very impressively, even when query sample of one modality is extremely degraded by random pixel corruption, illumination variation, etc., ABSRC can still get performance comparable to the unimodal recognition based on the other modality. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Multimodal recognition sparse coding Biometric quality assessment Adaptive feature fusion

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Learning Efficient Data Representations With Orthogonal sparse coding

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IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 2016年第3期2卷 177-189页

作者： Schuetze, Henry Barth, Erhardt Martinetz, Thomas Univ Lubeck Inst Neuro & Bioinformat D-23562 Lubeck Germany

We present the learning algorithm orthogonal sparse coding (OSC) to find an orthogonal basis in which a given data set has a maximally sparse representation. OSC is based on stochastic descent by Hebbian-like updates and Gram-Schmidt orthogonalizations, and is motivated by an algorithm that we introduce as the canonical approach (CA). First, we evaluate how well OSC can recover a generating basis from synthetic data. We show that, in contrast to competing methods, OSC can recover the generating basis for quite low and, remarkably, unknown sparsity levels. Moreover, on natural image patches and on images of handwritten digits, OSC learns orthogonal bases that attain significantly sparser representations compared to alternative orthogonal transforms. Furthermore, we demonstrate an application of OSC for image compression by showing that the rate-distortion performance can be improved relative to the JPEG standard. Finally, we demonstrate the state-of-the-art image denoising performance of OSC dictionaries. Our results demonstrate the potential of OSC for feature extraction, data compression, and image denoising, which is due to two important aspects: 1) the learned bases are adapted to the signal class, and 2) the sparse approximation problem can be solved efficiently and exactly.

关键词： Blind source separation dictionary learning image compression image denoising orthogonal mixture sparse coding sparse component analysis sparse representation transform coding

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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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