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Predicting human disease-associated circRNAs based on locality-constrained linear coding

GENOMICS 2020年第2期112卷 1335-1342页

作者： Ge, Erxia Yang, Yingjuan Gang, Mingjun Fan, Chunlong Zhao, Qi Liaoning Univ Sch Math Shenyang 110036 Peoples R China Jilin Univ Sch Math Changchun 130000 Peoples R China Shenyang Aerosp Univ Coll Comp Sci Shenyang 110136 Peoples R China

Circular RNAs (circRNAs) are a new kind of endogenous non-coding RNAs, which have been discovered continuously. More and more studies have shown that circRNAs are related to the occurrence and development of human diseases. Identification of circRNAs associated with diseases can contribute to understand the pathogenesis, diagnosis and treatment of diseases. However, experimental methods of circRNA prediction remain expensive and time-consuming. Therefore, it is urgent to propose novel computational methods for the prediction of circRNA-disease associations. In this study, we develop a computational method called LLCDC that integrates the known circRNA-disease associations, circRNA semantic similarity network, disease semantic similarity network, reconstructed circRNA similarity network, and reconstructed disease similarity network to predict circRNAs related to human diseases. Specifically, the reconstructed similarity networks are obtained by using locality-constrained linear coding (LLC) on the known association matrix, cosine similarities of circRNAs and diseases. Then, the label propagation method is applied to the similarity networks, and four relevant score matrices are respectively obtained. Finally, we use 5-fold cross validation (5-fold CV) to evaluate the performance of LLCDC, and the AUC value of the method is 0.9177, indicating that our method performs better than the other three methods. In addition, case studies on gastric cancer, breast cancer and papillary thyroid carcinoma further verify the reliability of our method in predicting disease-associated circRNAs.

关键词： circRNA Disease Association prediction Cosine similarity locality-constrained linear coding Label propagation

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Robust visual tracking via global context regularized locality-constrained linear coding

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OPTIK 2019年 183卷 232-240页

作者： Kang, Bin Liang, Dong Yang, Zhenzhen Nanjing Univ Posts & Telecommun Coll Internet Things Nanjing 210003 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Jiangsu Engn Res Ctr Commun & Network Technol Nanjing 210003 Jiangsu Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Sch Sci Nanjing 210003 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Natl Engn Res Ctr Commun & Networking Nanjing 210003 Jiangsu Peoples R China

locality-constrained linear coding (LLC) based visual tracking can give a better and faster tracking performance than traditional sparse representation based tracking methods. However, the existing LLC based methods often use the anchor points near the target to build the sparse coding dictionary for local sparse coding. It may cause a problem that it is hard to discriminate the difference between the negative and positive anchor points in sparse coding dictionary when facing severe background clutter, illumination change and occlusion. In this paper, we propose a context aware sparse coding method to achieve robust visual tracking. The proposed method can prevent the negative anchor points from disturbing the classifier accuracy because it uses a global context regularizer to constrain the sparse coding value of those negative anchor points that are similar to the positive anchor points. Experiment results show that our method can achieve a better tracking performance than state-of-the-art tracking methods do.

关键词： locality-constrained linear coding Global context Visual tracking

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Collaborative hashing adopted in locality-constrained linear coding for scene classification

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MULTIMEDIA TOOLS AND APPLICATIONS 2019年第12期78卷 16329-16343页

作者： Zhou, Zhiping Li, Chunye Zhao, Xiaoxiao Zhou, Fangzheng Jiangnan Univ Sch Internet Things Engn Wuxi 214122 Jiangsu Peoples R China Jiangnan Univ Engn Res Ctr Internet Things Technol Applicat Minist Educ Wuxi 214122 Jiangsu Peoples R China Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China

Scene classification methods based on effective feature extraction and coding have obtained promising results in recent years. But the K-nearest neighbor search strategy in locality-constrained linear coding (LLC) increases the complexity of the algorithm due to the exhaustive search. To solve the problem, an improved approximate nearest neighbor search strategy is proposed to improve the computational efficiency of LLC. Considering the mapping relationship between the visual words and features, a collaborative hashing method is incorporated to transform the high dimensional features into binary code form, and the original Euclidean space is transformed into the Hamming space that consists of multi similar features. The similar visual words can be queried quickly. Then the nearest neighbors can be searched efficiently through Hamming distance ranking, which can improve the coding efficiency. The experimental results on standard datasets demonstrate the effectiveness of the proposed approach, and the average classification accuracy can be improved.

关键词： Approximate nearest neighbor locality-constrained linear coding Collaborative hashing

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Visual tracking using locality-constrained linear coding and saliency map for visible light and infrared image sequences

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SIGNAL PROCESSING-IMAGE COMMUNICATION 2018年 68卷 13-25页

作者： Ding, Meng Yao Yuheng Wei, Li Cao, Yunfeng Nanjing Univ Aeronaut & Astronaut Coll Civil Aviat 29 Jiangjun St Nanjing Jiangsu Peoples R China Sci & Technol Electroopt Control Lab 25 Kaixuan St Luoyang Peoples R China Univ Southern Calif Viterbi Sch Engn 649 W 34th St Los Angeles CA USA Nanjing Univ Aeronaut & Astronaut Jin Cheng Coll 88 Hangjin St Nanjing Jiangsu Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Astronaut 29 Yudao St Nanjing Jiangsu Peoples R China

As a development of sparse coding, while retaining the advantage of sparse coding in classification, locality constrained linear coding(LLC) greatly improves the time efficiency of appearance modeling. However, in order to further promote the performance of real-time and develop a tracking algorithm that can be applied to both visible light images and infrared images, this paper proposes a tracking algorithm using LLC and saliency map under the framework of particle filtering. It is universally acknowledged that number of particles determines the accuracy of tracker under the framework of particle filtering. Unfortunately, the increase in the number of particles leads to the augment of computational burden. Therefore, the basic idea of the proposed algorithm is to reduce the computational number of observation vectors while keeping the effective number of particles and achieve the goal of strengthening the real-time performance of tracker. The proposed algorithm firstly uses spectral residual to obtain a saliency map of the current frame and then computes the saliency score of each particle. Secondly, several particles are eliminated directly according to the difference between the saliency score of the particle in the current frame and the target score in the previous frame. Thirdly, LLC is used to compute the observation vector for the rest particles and complete tracking tasks. Both quantitative and qualitative experimental results demonstrate that the proposed algorithm performs favorably against the nine state-of-the-art trackers on twelve challenging test sequences including six visible light sequences and six infrared sequences. In addition, related experimental results reveal that the proposed algorithm decreases the computational complexity and has the better tracking performance compared with the tracker just using LLC in the framework of particle filtering.

关键词： Visual tracking Visible light image Infrared image locality-constrained linear coding Spectral residual saliency Particle filtering

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Automatic vehicle detection and counting approach using low-rank representation and locality-constrained linear coding

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ENGINEERING COMPUTATIONS 2018年第8期35卷 2825-2843页

作者： Huangpeng, Qizi Huang, Wenwei Shi, Hanyi Fan, Jun Natl Univ Def Technol Dept Syst Engn Changsha Hunan Peoples R China Natl Univ Def Technol Dept Mil Basic Educ Changsha Hunan Peoples R China Natl Univ Def Technol Dept Space Sci & Engn Changsha Hunan Peoples R China

Purpose - Vehicles estimation can be used in evaluating traffic conditions and facilitating traffic control, which is an important task in intelligent transportation system. The paper aims to propose a vehicle-counting method based on the analysis of surveillance videos. Design/methodology/approach - The paper proposes a novel two-step method using low-rank representation (LRR) detection and locality-constrained linear coding (LLC) classification to count the number of vehicles in traffic video sequences automatically. The proposed method is based on an offline training to understand an LLC-based classifier with extracted features for vehicle and pedestrian classification, followed by an online counting algorithm to count the number of vehicles detected from the image sequence. Findings - The proposed method allows delivery estimation (counting the number of vehicles at each frame only) and total number estimation of vehicles shown in the scene. The paper compares the proposed method with other similar methods on three public data sets. The experimental results show that the proposed method is competitive and effective in terms of computational speed and evaluation accuracy. Research limitations/implications - The proposed method does not consider illumination. Hence, the results might be unsatisfactory under low-lighting condition. Therefore, researchers are encouraged to add a term that controls the illumination changes into the energy function of vehicle detection in future work. Originality/value - The paper bridges the gap between LRR detection and vehicle counting by taking advantage of existing LLC classification algorithm to distinguish different moving objects.

关键词： locality-constrained linear coding Low-rank representation Vehicle counting

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Computing object-based saliency via locality-constrained linear coding and conditional random fields

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VISUAL COMPUTER 2017年第11期33卷 1403-1413页

作者： Yang, Zhen Xiong, Huilin Shanghai Jiao Tong Univ Dept Automat 800 Dongchuan Rd Shanghai 200240 Peoples R China

Predicting object location using a top-down saliency model has grown increasingly popular in recent years. In this work, we combine locality-constrained linear coding (LLC) with a conditional random field (CRF), and construct a top-down saliency model to generate a specific object-based saliency map. During the training phase, we use the LLC codes as the latent variables of the CRF model, and meanwhile learn a class-specific codebook by CRF modulation. In the testing phase, we use this top-down model to distinguish specific objects from a cluttered background. Finally, we evaluate the experimental results on the MSRA-B, Garz-02, Weizmann Horse, and Plane datasets by applying the developed object-based saliency model. The performance shows that our approach can not only improve the precision but also dramatically reduce the computational complexity.

关键词： Top-down model locality-constrained linear coding Conditional random field Object-based saliency

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Discriminative human action classification using locality-constrained linear coding

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PATTERN RECOGNITION LETTERS 2016年 72卷 62-71页

作者： Rahmani, Hossein Huynh, Du Q. Mahmood, Arif Mian, Ajmal Univ Western Australia Sch Comp Sci & Software Engn Crawley WA 6009 Australia Univ Western Australia Sch Math & Stat Crawley WA 6009 Australia

We propose a locality-constrained linear coding (LLC) based algorithm that captures discriminative information of human actions in spatio-temporal subsequences of videos. The input video is divided into equally spaced overlapping spatio-temporal subsequences. Each subsequence is further divided into blocks and then cells. The spatio-temporal information in each cell is represented by a Histogram of Oriented 3D Gradients (HOG3D). LLC is then used to encode each block. We show that LLC gives more stable and repetitive codes compared to the standard Sparse coding. The final representation of a video sequence is obtained using logistic regression with l(2) regularization and classification is performed by a linear SVM. The proposed algorithm is applicable to conventional and depth videos. Experimental comparison with ten state-of-the-art methods on three depth video and two conventional video databases shows that the proposed method consistently achieves the best performance. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Human action classification locality-constrained linear coding Sparse coding SVM classifier

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Classification of time-frequency images based on locality-constrained linear coding optimization model for rotating machinery fault diagnosis

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2015年第18期229卷 3350-3360页

作者： Wang, Wei-Gang Liu, Zhan-Sheng Harbin Inst Technol Sch Energy Sci & Engn 92 West Da Zhi St Harbin 150001 Heilongjiang Peoples R China Northeast Petr Univ Coll Mech Sci & Engn Daqing Heilongjiang Peoples R China

Time-frequency images have been widely employed in vibration signal analysis in the field of rotating machinery fault diagnosis. Generally, global features such as gray statistic features and texture features, are extracted from time-frequency images for fault classification, but the effect is not satisfactory. The locality-constrained linear coding model based on local features has been successfully employed in image classification. This paper contributes to this ongoing investigation by developing a locality-constrained linear coding optimization model applied in time-frequency images classification for rotating machinery fault diagnosis. The classification accuracy and generalization of the locality-constrained linear coding model depend on the selection of pooling methods, values of pooling parameters, and penalization coefficient. In the optimization model, misclassification rate is chosen as an objective function, and an improved particle swarm optimization algorithm is adopted to optimize the pooling parameters and penalization coefficient. The improved particle swarm optimization algorithm is utilized to produce optimal solutions at the training stage, and then these solutions will be evaluated at the testing stage. The promise of the novel model is illustrated by performing our procedure on vibration signals from a rolling bearing with 16 health states. Experimental results have demonstrated that the proposed approach could obviously increase the classification accuracy.

关键词： Time-frequency images locality-constrained linear coding optimization model improved particle swarm optimization rotating machinery fault diagnosis

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Recognising lepidopteran images based on locality-constrained linear coding and SVM

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ORIENTAL INSECTS 2015年第1-2期49卷 36-46页

作者： Zhu, Leqing Zhang, Zhen Zhang, Daxing Zhejiang Gongshang Univ Sch Comp Sci & Informat Engn Hangzhou Zhejiang Peoples R China Chinese Acad Forestry Res Inst Forest Ecol Environm & Protect State Forestry Adm Key Lab Forest Protect Beijing Peoples R China Hangzhou Dianzi Univ Inst Graph & Image Hangzhou Zhejiang Peoples R China

In this paper, a novel lepidopteran insect images recognition method is proposed. The captured insect images are first preprocessed to separate the foreground from the complicated background. Then, two wings are cut out from the insect, calibrated and segmented into a number of superpixels. The values of l, a, b, x, y of each superpixel are calculated as feature descriptors. Those descriptors are encoded with locality-constrained linear coding and then pooled into a feature vector. Finally, the multi-class linear support vector machine is used for classification. The approach is testified in a data-set including 576 images of insect specimens from 10 different lepidopteran species and the recognition accuracy is over 99% in average. The experimental results suggested that the proposed method performs well in lepidopteran insect species recognition.

关键词： lepidoptera image recognition superpixels locality-constrained linear coding SVM

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Object Tracking based on locality-constrained linear coding Joint Sparse Representation Appearance Model

Object Tracking based on Locality-constrained Linear Coding ...

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2015 IEEE 16th International Conference on Communication Technology(ICCT 2015)

作者： Feibin Li Tieyong Cao Zhijun Song Wen Wang PLA University of Science and Technology The 28th Research Institute of China Electronics Technology Group Corporation

ISBN: (纸本)9781467370066

Focusing on the robust object tracking, we propose an algorithm combining discriminative model with generative model. In the discriminative model, we exploit the prior visual information to learn an over-complete dictionary, and use the locality constrained linear(LLC)coding to represent the object. Then use the linear SVM classifier to separate the foreground from the background to implement object tracking. In the generative model, we propose a sparse generative model to partition the object into patches and take the occlusion factor into account to construct object templates. Then use the particle filter to evaluate the target position. Finally joint the two models to acquire final tracking result. In addition, in order to handle the object appearance variation caused by occlusion, fast motion, illumination change and background clutter, we make a simple yet effective update scheme. Both qualitative and quantitative evaluations on challenging image sequences demonstrate that the proposed algorithm performs favorably against several state-of-the-art methods.

关键词： sparse representation particle filter appearance model locality-constrained linear coding object tracking

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