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Protein fold recognition based on sparse representation based classification

ARTIFICIAL INTELLIGENCE IN MEDICINE 2017年第Jun.期79卷 1-8页

作者： Yan, Ke Xu, Yong Fang, Xiaozhao Zheng, Chunhou Liu, Bin Harbin Inst Technol Shenzhen Grad Sch Sch Comp Sci & Technol Shenzhen 518055 Guangdong Peoples R China Anhui Univ Coll Elect Engn & Automat Hefei 230039 Anhui Peoples R China

Knowledge of protein fold type is critical for determining the protein structure and function. Because of its importance, several computational methods for fold recognition have been proposed. Most of them are based on well-known machine learning techniques, such as Support Vector Machines (SVMs), Artificial Neural Network (ANN), etc. Although these machine learning methods play a role in stimulating the development of this important area, new techniques are still needed to further improve the predictive performance for fold recognition. sparse representation based classification (SRC) has been widely used in image processing, and shows better performance than other related machine learning methods. In this study, we apply the SRC to solve the protein fold recognition problem. Experimental results on a widely used benchmark dataset show that the proposed method is able to improve the performance of some basic classifiers and three state-of-the-art methods to feature selection, including autocross-covariance (ACC) fold, D-D, and Bi-gram. Finally, we propose a novel computational predictor called MF-SRC for fold recognition by combining these three features into the framework of SRC to achieve further performance improvement. Compared with other computational methods in this field on DD dataset, EDD dataset and TG dataset, the proposed method achieves stable performance by reducing the influence of the noise in the dataset. It is anticipated that the proposed predictor may become a useful high throughput tool for large-scale fold recognition or at least, play a complementary role to the existing predictors in this regard. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Protein fold recognition sparse representation based classification Protein representation

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Semi-Supervised sparse representation based classification for Face Recognition With Insufficient Labeled Samples

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2017年第5期26卷 2545-2560页

作者： Gao, Yuan Ma, Jiayi Yuille, Alan L. Wuhan Univ Elect Informat Sch Wuhan 430072 Peoples R China Tencent AI Lab Shenzhen 518057 Peoples R China Univ Calif Los Angeles Dept Stat Los Angeles CA 90095 USA Johns Hopkins Univ Dept Cognit Sci Dept Comp Sci Baltimore MD 21218 USA

This paper addresses the problem of face recognition when there is only few, or even only a single, labeled examples of the face that we wish to recognize. Moreover, these examples are typically corrupted by nuisance variables, both linear (i.e., additive nuisance variables, such as bad lighting and wearing of glasses) and non-linear (i.e., non-additive pixel-wise nuisance variables, such as expression changes). The small number of labeled examples means that it is hard to remove these nuisance variables between the training and testing faces to obtain good recognition performance. To address the problem, we propose a method called semi-supervised sparse representation-based classification. This is based on recent work on sparsity, where faces are represented in terms of two dictionaries: a gallery dictionary consisting of one or more examples of each person, and a variation dictionary representing linear nuisance variables (e.g., different lighting conditions and different glasses). The main idea is that: 1) we use the variation dictionary to characterize the linear nuisance variables via the sparsity framework and 2) prototype face images are estimated as a gallery dictionary via a Gaussian mixture model, with mixed labeled and unlabeled samples in a semi-supervised manner, to deal with the non-linear nuisance variations between labeled and unlabeled samples. We have done experiments with insufficient labeled samples, even when there is only a single labeled sample per person. Our results on the AR, Multi-PIE, CAS-PEAL, and LFW databases demonstrate that the proposed method is able to deliver significantly improved performance over existing methods.

关键词： Gallery dictionary learning semi-supervised learning face recognition sparse representation based classification single labeled sample per person

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HIGH-ORDER DIRECTIONAL FEATURES AND sparse representation based classification FOR IN-AIR HANDWRITTEN CHINESE CHARACTER RECOGNITION

HIGH-ORDER DIRECTIONAL FEATURES AND SPARSE REPRESENTATION BA...

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IEEE International Conference on Multimedia & Expo (ICME)

作者： Qu, Xiwen Wang, Weiqiang Lu, Ke Ji, Zhangjian Univ Chinese Acad Sci Sch Comp & Control Engn Beijing Peoples R China Shanxi Univ Sch Comp & Informat Technol Taiyuan Peoples R China

ISBN: (纸本)9781467372589

The in-air handwriting is a natural and promising human-computer interaction way. Compared with handwritten Chinese characters on touch screen, the in-air handwritten Chinese characters have their unique characteristics, e.g., each character is always written in a single stroke. In this paper, we propose a high-order directional feature for recognizing in-air handwritten Chinese characters. The proposed high-order features characterize the rate of direction change and the change rate of the rate of direction change, and can be easily be combined with the 8-directional features to get an enhanced version. Additionally, we exploit the locality-sensitive dictionary learning and sparse representation based classifier (LSRC) to recognize in-air handwritten Chinese characters. Since few work has applied the SRC in on-line handwritten Chinese character recognition (OHCCR), we evaluate the proposed system on both an in-air hand-written Chinese character dataset, the IAHCC-UCAS2015 dataset, and a handwritten Chinese character dataset, the SCUT-COUCH2009 database. The experimental results show that the proposed high-order directional features, when combined with the first-order directional features (8-directional features), can improve the recognition accuracy, and they are more suitable for IAHCCR than traditional OHCCR. Additionally, the results also demonstrate the LSRC is a good choice for IAHCCR.

关键词： high-order directional features in-air handwritten Chinese character recognition sparse representation based classification

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Simultaneous discriminative projection and dictionary learning for sparse representation based classification

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PATTERN RECOGNITION 2013年第1期46卷 346-354页

作者： Zhang, Haichao Zhang, Yanning Huang, Thomas S. Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Univ Illinois Beckman Inst Urbana IL 61801 USA

Sparsity driven classification method has been popular recently due to its effectiveness in various classification tasks. It is based on the assumption that samples of the same class live in the same subspace, thus a test sample can be well represented by the training samples of the same class. Previous methods model the subspace for each class with either the training samples directly or dictionaries trained for each class separately. Although enabling strong reconstructive ability, these methods may not have desirable discriminative ability, especially when there are high correlations among the samples of different classes. In this paper, we propose to learn simultaneously a discriminative projection and a dictionary that are optimized for the sparse representation based classifier, to extract discriminative information from the raw data while respecting the sparse representation assumption. By formulating the task of projection and dictionary learning into an optimization framework, we can learn the discriminative projection and dictionary effectively. Extensive experiments are carried out on various datasets and the experimental results verify the efficacy of the proposed method. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Discriminative projection learning Dictionary learning Metric learning sparse representation based classification

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Compound Fault Diagnosis of Gearbox based on Wavelet Packet Transform and sparse representation classification 32

Compound Fault Diagnosis of Gearbox Based on Wavelet Packet ...

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32nd Chinese Control And Decision Conference (CCDC)

作者： Yu Fa-jun Liu Yi-cai Zhao Qi-feng Zhongyuan Univ Technol Sch Elect & Informat Engineer Zhengzhou 450007 Peoples R China Wuhan Business Univ Dept Mechatron Engn & Automot Serv Wuhan 430056 Hubei Peoples R China

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158556

Gearboxes are widely used elements in mechanical equipment. Accurate fault diagnosis of the gearbox is essential to ensure the normal operation of the entire equipment. Owing to the complexity of vibration signal when gearbox compound fault occurs, it is quite difficult to diagnose compound fault of gearbox with *** this paper, a novel gearbox compound fault diagnosis method is presented, which takes advantage of Wavelet Packet Transform(WPT) extracting fault feature components and determines the type of compound fault according to the results of sparse representation based classification (SRC). In the proposed method, WPT is adopted to filter the gearbox vibration signal of unknown fault type into different frequency-band components in the first step, then Distance Evaluation Parameter(DEP) is utilized to select several feature components from these frequency-band components, and finally, SRC is used to categorize the selected feature components on a dictionary constructed with samples of known fault types according to the location of the minimum value of their sparse reconstruction errors. The results of gearbox experiment verify the effectiveness of the proposed method.

关键词： Compound Fault Diagnosis Gearbox Wavelet Packet Transform sparse representation based classification

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Compound Fault Diagnosis of Rolling Bearing based on Tunable Q-Factor Wavelet Transform and sparse representation classification 32

Compound Fault Diagnosis of Rolling Bearing Based on Tunable...

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32nd Chinese Control And Decision Conference (CCDC)

作者： Guo, Chujian Liu, Yicai Yu, Fajun Wuhan Business Univ Wuhan 430056 Hubei Peoples R China Zhongyuan Univ Technol Zhengzhou 450007 Henan Peoples R China

ISBN: (纸本)9781728158556

Rolling bearing is a vital component of many mechanical equipment, but it's easy to damage, especially in harsh working conditions for a long time. This damage is either caused by a single failure or caused by a composite failure. The paper presents a new compound fault diagnosis method of rolling bearing, in which Wavelet Transform (WT) for feature extraction and sparse representation based classification (SRC) for diagnosis are comprehensively applied. Firstly, the Tunable Q-Factor WT is performed on bearing vibration signals to extract fault features at different frequency bands. Then, the fault features in each band are coded sparsely on the established training sample dictionary set, and the respective fault feature bands are reconstructed using the sparse coefficient. Finally, the composite fault type of the bearing fault is judged according to the fault category where the minimum value is located. The effectiveness of the proposed method is verified by the simulation experiment and the bearing failure experiment.

关键词： Composite failure Fault Diagnosis Tunable Q-Factor Wavelet Transform sparse representation based classification

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Compound Fault Diagnosis of Rolling Bearing based on Tunable Q-Factor Wavelet Transform and sparse representation classification

Compound Fault Diagnosis of Rolling Bearing Based on Tunable...

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第32届中国控制与决策会议

作者： Chujian Guo Yicai Liu Fajun Yu Wuhan Business University Zhongyuan University of Technology

Rolling bearing is a vital component of many mechanical equipment,but it's easy to damage,especially in harsh working conditions for a long *** damage is either caused by a single failure or caused by a composite *** paper presents a new compound fault diagnosis method of rolling bearing,in which Wavelet Transform(WT) for feature extraction and sparse representation based classification(SRC) for diagnosis are comprehensively ***,the Tunable Q-Factor WT is performed on bearing vibration signals to extract fault features at different frequency ***,the fault features in each band are coded sparsely on the established training sample dictionary set,and the respective fault feature bands are reconstructed using the sparse ***,the composite fault type of the bearing fault is judged according to the fault category where the minimum value is *** effectiveness of the proposed method is verified by the simulation experiment and the bearing failure experiment.

关键词： Composite failure Fault Diagnosis Tunable Q-Factor Wavelet Transform sparse representation based classification

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Compound Fault Diagnosis of Gearbox based on Wavelet Packet Transform and sparse representation classification

Compound Fault Diagnosis of Gearbox Based on Wavelet Packet ...

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第32届中国控制与决策会议

作者： Yu Fa-jun Liu Yi-cai Zhao Qi-feng School of Electronic and Information Engineer Zhongyuan University of Technology Department of Mechatronics Engineering and Automotive Services Wuhan Business University

Gearboxes are widely used elements in mechanical *** fault diagnosis of the gearbox is essential to ensure the normal operation of the entire *** to the complexity of vibration signal when gearbox compound fault occurs,it is quite difficult to diagnose compound fault of gearbox with *** this paper,a novel gearbox compound fault diagnosis method is presented,which takes advantage of Wavelet Packet Transform(WPT) extracting fault feature components and determines the type of compound fault according to the results of sparse representation based classification(SRC).In the proposed method,WPT is adopted to filter the gearbox vibration signal of unknown fault type into different frequency-band components in the first step,then Distance Evaluation Parameter(DEP) is utilized to select several feature components from these frequency-band components,and finally,SRC is used to categorize the selected feature components on a dictionary constructed with samples of known fault types according to the location of the minimum value of their sparse reconstruction *** results of gearbox experiment verify the effectiveness of the proposed method.

关键词： Compound Fault Diagnosis Gearbox Wavelet Packet Transform sparse representation based classification

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Single sample per person face recognition algorithm based on the robust prototype dictionary and robust variation dictionary construction

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IET IMAGE PROCESSING 2022年第3期16卷 742-754页

作者： Xue, Shan Ren, Hai-Peng Xian Aeronaut Univ Fac Comp Sci Xian Peoples R China Xian Technol Univ Sch Mechatron Engn Xian Peoples R China

Single sample per person (SSPP) face recognition uses only a single face image of each subject in the gallery set to recognize the probe sample. Since single sample cannot provide intra-class variation information, the matching accuracy of the gallery faces with the faces captured in unconstrained video is usually low. Recently, in order to improve the accuracy, the sparse representation-based classification (SRC) technology has been extended to generic learning method, which uses prototype and variation dictionary (P+V) model for face recognition. Because the inter-class scatter between atoms in prototype dictionary is not big enough, and the intra-class scatter in constructed variation dictionary (such as posture and expression) is not rich enough, the robustness of P+V model for SSPP face recognition is poor. To solve this problem, a robust prototype dictionary and robust variation dictionary construction (RPRV) method is proposed. First, a set of atoms is obtained by dictionary learning method using gallery images and generic images. Second, some effective atoms are selected by the proposed function index method. Finally, the robust prototype dictionary (RP) and the robust variation dictionary (RV) are represented linearly using these effective atoms, respectively. The face recognition is performed according to the proposed RP+RV model. Experiment results using public datasets show that the proposed RPRV has strong robustness for the face captured under the unconstrained environment. Comparison results show that the proposed RPRV method outperform state-of-the-art SRC-based methods for SSPP face recognition.

关键词： generic learning method dictionary learning method intraclass scatter generic images single face image SRC technology SSPP face recognition gallery faces image representation RPRV learning (artificial intelligence) interclass scatter video signal processing intraclass variation information face recognition robust variation dictionary construction method single sample per person face recognition algorithm robust prototype dictionary gallery images SSPP sparse representation based classification image classification

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Face Recognition: A sparse representation-based classification Using Independent Component Analysis

Face Recognition: A Sparse Representation-based Classificati...

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6th International Symposium on Telecommunications (IST) with Emphasis on Information and Communication Technology

作者： Karimi, Mirhossein Mousavi Soltanian-Zadeh, Hamid Univ Tehran Dept Elect & Comp Engn Tehran 14174 Iran

ISBN: (纸本)9781467320733

In this paper, we will describe a new method based on sparse representation-based classification (SRC) for face recognition. We have used histogram equalization as a preprocessing method in order to overcome the illumination variation problem. Using Independent Component Analysis we have obtained a feature vector for each face image which is robust to illumination variations and occlusion. Although SRC is robust against occlusion, it is rather slow. By using features with smaller dimensions but enough information, we can obtain better recognition rates in shorter periods. This method was tested on Extended Yale B database and obtained the recognition rates of 98.51% and 95.77% in presence of 10% and 20% occlusion, respectively.

关键词： Face Recognition ICA sparse representation based classification

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