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18th Pacific-Rim Conference on Multimedia (PCM)

作者： Rui, Ting Zhang, Sai Ren, Tongwei Tang, Jian Zou, Junhua PLA Univ Sci & Technol Nanjing 210007 Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Peoples R China

ISBN: (纸本)9783319773834;9783319773827

Digital media information reconstruction has attracted much attention in machine learning, we propose a new method about this problem for supervising learning by using the classical unsupervised auto-encoders (AE), and we also analyze the deep model structure and training strategy. In this paper, we present a supervised deep-based auto-encoder model which has a set of progressive and interrelated learning strategies by multiple groups of supervised single-layer AE. In this structure, the one-to-one training strategy in classical AE model (one output corresponding to one input) is substituted by the multi-to-one training strategy (one output corresponding to many inputs), and it improves the ability to express the feature code. We use the structure and training strategy mentioned above to reconstruct the damaged or obscured images. Experimental results show that the proposed method has good effect and adaptability to the reconstruction of the damaged or occluded samples.

关键词： auto-encoder Supervised learning Deep structure Training strategy Image reconstruction

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Dimensionality Reduction for Hyperspectral Data Based on Sample-Dependent Repulsion Graph Regularized auto-encoder

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Chinese Journal of Electronics 2017年第6期26卷 1233-1238页

作者： WANG Xuesong KONG Yi CHENG Yuhu School of Information and Control Engineering China University of Mining and Technology

To achieve high classification accuracy of hyperspectral data, a dimensionality reduction algorithm called Sample-dependent repulsion graph regularized auto-encoder(SRGAE) is proposed. Based on the sample-dependent graph, by applying the repulsion force to the samples from different classes but nearby, a sampledependent repulsion graph is built to make the samples from the same class will be pro jected to samples that are close-by and the samples from different classes will be pro jected to samples that are far away. The sampledependent repulsion graph can avoid the neighborhood parameter selection problem existing in the nearest neighborhood graph. By integrating advantages of deep learning and graph regularization technique, the SRGAE can maintain the learned deep features are consistent with the inherent manifold structure of the original hyperspectral data. Experimental results on two real hyperspectral data show that, when compared with some popular dimensionality reduction algorithms, the proposed SRGAE can yield higher classification accuracy.

关键词： Hyperspectral data Dimensionality reduction Sample-dependent repulsion graph Regularized auto-encoder

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Bearing fault diagnosis with auto-encoder extreme learning machine: A comparative study

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2017年第8期231卷 1560-1578页

作者： Mao, Wentao He, Jianliang Li, Yuan Yan, Yunju Henan Normal Univ Sch Comp & Informat Engn Xinxiang 453007 Peoples R China Northwestern Polytech Univ Sch Mech & Civil & Architecture Xian Peoples R China

Intelligent fault-diagnosis methods using machine learning techniques like support vector machines and artificial neural networks have been widely used to distinguish bearings' health condition. However, though these methods generally work well, they still have two potential drawbacks when facing massive fault data: (1) the feature extraction process needs prior domain knowledge, and therefore lacks a universal extraction method for various diagnosis issues, and (2) much training time is generally needed by the traditional intelligent diagnosis methods and by the newly presented deep learning methods. In this research, inspired by the feature extraction capability of auto-encoders and the high training speed of extreme learning machines (ELMs), an auto-encoder-ELM-based diagnosis method is proposed for diagnosing faults in bearings to overcome the aforementioned deficiencies. This paper performs a comparative analysis of the proposed method and some state-of-the-art methods, and the experimental results on the rolling element bearings data set show the effectiveness of the proposed method not only with adaptive mining of the discriminative fault characteristic but also at high diagnosis speed.

关键词： Intelligent fault diagnosis auto-encoder deep learning massive data bearings

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Design of Ensemble Stacked auto-encoder for Classification of Horse Gaits with MEMS Inertial Sensor Technology

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MICROMACHINES 2018年第8期9卷 411-411页

作者： Lee, Jae-Neung Byeon, Yeong-Hyeon Kwak, Keun-Chang Chosun Univ Dept Control & Instrumentat Engn 375 Seosuk Dong Gwangju 501759 South Korea

This paper discusses the classification of horse gaits for self-coaching using an ensemble stacked auto-encoder (ESAE) based on wavelet packets from the motion data of the horse rider. For this purpose, we built an ESAE and used probability values at the end of the softmax classifier. First, we initialized variables such as hidden nodes, weight, and max epoch using the options of the auto-encoder (AE). Second, the ESAE model is trained by feedforward, back propagation, and gradient calculation. Next, the parameters are updated by a gradient descent mechanism as new parameters. Finally, once the error value is satisfied, the algorithm terminates. The experiments were performed to classify horse gaits for self-coaching. We constructed the motion data of a horse rider. For the experiment, an expert horse rider of the national team wore a suit containing 16 inertial sensors based on a wireless network. To improve and quantify the performance of the classification, we used three methods (wavelet packet, statistical value, and ensemble model), as well as cross entropy with mean squared error. The experimental results revealed that the proposed method showed good performance when compared with conventional algorithms such as the support vector machine (SVM).

关键词： motion analysis auto-encoder dance classification deep learning self-coaching wavelet packet classification of horse gaits

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An auto-encoder-based summarization algorithm for unstructured videos

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MULTIMEDIA TOOLS AND APPLICATIONS 2017年第23期76卷 25039-25056页

作者： Han, Meng-Xiong Hu, Hai-Miao Liu, Yang Zhang, Chi Tian, Rong-Peng Zheng, Jin Beihang Univ Sch Comp Sci & Engn Beijing Key Lab Digital Media Beijing 100083 Peoples R China Beijing Inst Graph Beijing 100029 Peoples R China Beihang Univ Digital Media Lab Beijing Peoples R China

Video summarization is an effective way to quick view videos and relieve the pressure of videos storage. However the traditional algorithms are hardly adapted to unstructured videos, due to the unobvious for scenes changing and ignoring the structure of the videos. Therefore, an auto-encoder-based summarization algorithm is proposed in this paper for unstructured videos. Each video structure is detected by an auto-encoder and both of the interestingness and representativeness of each video segment are predicted by the reconstruction errors of the segment. Meanwhile, most interesting and representative summarization is generated with the limited summary length. The experimental results show that the proposed algorithm obtained a better performance by comparing with the state-of-the-art.

关键词： Video summarization auto-encoder Video analysis

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Age invariant face recognition and retrieval by coupled auto-encoder networks

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NEUROCOMPUTING 2017年 222卷 62-71页

作者： Xu, Chenfei Liu, Qihe Ye, Mao Univ Elect Sci & Technol China Sch Comp Sci & Engn Ctr Robot Chengdu 611731 Peoples R China

Recently many promising results have been shown on face recognition related problems. However, age invariant face recognition and retrieval remains a challenge. Inspired by the observation that age variation is a nonlinear but smooth transform and the ability of auto-encoder network to learn latent representations from inputs, in this paper, we propose a new neural network model called coupled auto-encoder networks (CAN) to handle age-invariant face recognition and retrieval problem. CAN is a couple of two auto-encoders which bridged by two shallow neural networks used to fit complex nonlinear aging and de-aging process. We further propose a nonlinear factor analysis method to nonlinearly decompose one given face image into three components which are identity feature, age feature and noise, where identity feature is age-invariant and can be used for face recognition and retrieval. Experiments on three public available face aging datasets: FGNET, CACD and CACD-VS show the, effectiveness of the proposed approach.

关键词： Face recognition Age invariant auto-encoder

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Application of Improved Deep auto-encoder Network in Rolling Bearing Fault Diagnosis

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Journal of Computer and Communications 2018年第7期6卷 41-53页

作者： Jian Di Leilei Wang School of Control and Computer Engineering North China Electric Power University Baoding China

Since the effectiveness of extracting fault features is not high under traditional bearing fault diagnosis method, a bearing fault diagnosis method based on Deep auto-encoder Network (DAEN) optimized by Cloud Adaptive Particle Swarm Optimization (CAPSO) was proposed. On the basis of analyzing CAPSO and DAEN, the CAPSO-DAEN fault diagnosis model is built. The model uses the randomness and stability of CAPSO algorithm to optimize the connection weight of DAEN, to reduce the constraints on the weights and extract fault features adaptively. Finally, efficient and accurate fault diagnosis can be implemented with the Softmax classifier. The results of test show that the proposed method has higher diagnostic accuracy and more stable diagnosis results than those based on the DAEN, Support Vector Machine (SVM) and the Back Propagation algorithm (BP) under appropriate parameters.

关键词： Fault Diagnosis Rolling Bearing Deep auto-encoder Network CAPSO Algorithm Feature Extraction

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auto-encoder Based Clustering Algorithms for Intuitionistic Fuzzy Sets 12

Auto-encoder Based Clustering Algorithms for Intuitionistic ...

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12th International Conference on Intelligent Systems and Knowledge Engineering (IEEE ISKE)

作者： Du, Yimin Wu, Guixing Tang, Guolin Univ Sci & Technol China Sch Software Engn Hefei Anhui Peoples R China Beijing Univ Technol Sch Econ & Management Beijing Peoples R China

ISBN: (纸本)9781538618295

Clustering plays an important role in data mining and machine learning. Then, intuitionistic fuzzy sets (IFSs) are flexible and practical in dealing with vagueness and uncertainty problems. To cluster the information expressed by intuitionistic fuzzy data, this paper proposes the joint training auto-encoder based intuitionistic fuzzy clustering algorithm. Firstly, we propose the auto-encoder based intuitionistic fuzzy clustering by utilizing similarity measure of IFSs, auto encoder and k-means algorithm. Then, we propose the joint training auto-encoder based intuitionistic fuzzy clustering algorithm by utilizing the proposed auto encoder based intuitionistic fuzzy clustering and two kinds of similarity measures for the clustering analysis of intuitionistic fuzzy data. Lastly, several experiments are provided to verify the effectiveness of the proposed intuitionistic fuzzy clustering algorithms.

关键词： intuitionistic fuzzy set similarity measure auto-encoder clustering

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A deep learning method for lincRNA detection using auto-encoder algorithm

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BMC BIOINFORMATICS 2017年第15-Sup期18卷 511-511页

作者： Yu, Ning Yu, Zeng Pan, Yi SUNY Coll Brockport Dept Comp Sci 350 New Campus Dr Brockport NY 14420 USA Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 610031 Sichuan Peoples R China Georgia State Univ Dept Comp Sci 25 Pk Pl Atlanta GA 30303 USA

Background: RNA sequencing technique (RNA-seq) enables scientists to develop novel data-driven methods for discovering more unidentified lincRNAs. Meantime, knowledge-based technologies are experiencing a potential revolution ignited by the new deep learning methods. By scanning the newly found data set from RNA-seq, scientists have found that: (1) the expression of lincRNAs appears to be regulated, that is, the relevance exists along the DNA sequences;(2) lincRNAs contain some conversed patterns/motifs tethered together by non-conserved regions. The two evidences give the reasoning for adopting knowledge-based deep learning methods in lincRNA detection. Similar to coding region transcription, non-coding regions are split at transcriptional sites. However, regulatory RNAs rather than message RNAs are generated. That is, the transcribed RNAs participate the biological process as regulatory units instead of generating proteins. Identifying these transcriptional regions from non-coding regions is the first step towards lincRNA recognition. Results: The auto-encoder method achieves 100% and 92.4% prediction accuracy on transcription sites over the putative data sets. The experimental results also show the excellent performance of predictive deep neural network on the lincRNA data sets compared with support vector machine and traditional neural network. In addition, it is validated through the newly discovered lincRNA data set and one unreported transcription site is found by feeding the whole annotated sequences through the deep learning machine, which indicates that deep learning method has the extensive ability for lincRNA prediction. Conclusions: The transcriptional sequences of lincRNAs are collected from the annotated human DNA genome data. Subsequently, a two-layer deep neural network is developed for the lincRNA detection, which adopts the auto-encoder algorithm and utilizes different encoding schemes to obtain the best performance over intergenic DNA sequence da

关键词： Deep learning Long intergenic non-coding RNA (lincRNA) auto-encoder Transcription sites RNA-seq Knowledge-based discovery

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Pareto-optimal multi-objective dimensionality reduction deep auto-encoder for mammography classification

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017年 145卷 85-93页

作者： Taghanaki, Saeid Asgari Kawahara, Jeremy Miles, Brandon Hamarneh, Ghassan Simon Fraser Univ Med Image Anal Lab Burnaby BC Canada

Background and objective: Feature reduction is an essential stage in computer aided breast cancer diagnosis systems. Multilayer neural networks can be trained to extract relevant features by encoding high-dimensional data into low-dimensional codes. Optimizing traditional auto-encoders works well only if the initial weights are close to a proper solution. They are also trained to only reduce the mean squared reconstruction error (MRE) between the encoder inputs and the decoder outputs, but do not address the classification error. The goal of the current work is to test the hypothesis that extending traditional auto-encoders (which only minimize reconstruction error) to multi-objective optimization for finding Pareto-optimal solutions provides more discriminative features that will improve classification performance when compared to single-objective and other multi-objective approaches (i.e. scalarized and sequential). Methods: In this paper, we introduce a novel multi-objective optimization of deep auto-encoder networks, in which the auto-encoder optimizes two objectives: MRE and mean classification error (MCE) for Pareto-optimal solutions, rather than just MRE. These two objectives are optimized simultaneously by a non-dominated sorting genetic algorithm. Results: We tested our method on 949 X-ray mammograms categorized into 12 classes. The results show that the features identified by the proposed algorithm allow a classification accuracy of up to 98.45%, demonstrating favourable accuracy over the results of state-of-the-art methods reported in the literature. Conclusions: We conclude that adding the classification objective to the traditional auto-encoder objective and optimizing for finding Pareto-optimal solutions, using evolutionary multi-objective optimization, results in producing more discriminative features. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Breast cancer Computer aided diagnosis Feature reduction auto-encoder Multi-objective optimization

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