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Multi-view deep unsupervised transfer leaning via joint auto-encoder coupled with dictionary learning

INTELLIGENT DATA ANALYSIS 2019年第3期23卷 555-571页

作者： Diasse, Abdoullahi Li, Zhiyong Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Hunan Peoples R China

Transfer learning empowers machine learning algorithms the ability to train a model on a given task, capture the existing relationship in the data and reuse it for another task in the same or similar domain. In this paper, we present a Multiview deep unsupervised transfer leaning via joint auto-encoder coupled with dictionary learning (MVT-DAE). In the proposed approach knowledge transfer is done in two stages. First, we perform multi-view dictionary learning based on low-rank tensor regularization in the source domain to learn the common intrinsic relationship among views. Then we transfer the learned dictionaries to the target domain, and we construct a new representation of both domain via sparse coding. In the second stage, we use two deep auto-encoders (DAE) from both sources to perform parameters transfer. Each DAE consist of an embedding layer and a label layer. The embedding layer reconstructs the input while the label layer is used to encode the label information. To relax the distribution distance of the embedding and label layer between the source and the target domain a joint maximum mean discrepancy (JMMD) is employed. Learning can be done via Stochastic gradient descent by sharing the embedding and label layer weights with the target domain. We conduct extensive experiments in two real-world datasets which demonstrated the effectiveness of our approach compared with the different state of the art baseline.

关键词： Transfer learning multi-view deep auto-encoder dictionary learning

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A multi-ensemble method based on deep auto-encoders for fault diagnosis of rolling bearings

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MEASUREMENT 2020年 151卷 107132-000页

作者： Kong, Xianguang Mao, Gang Wang, Qibin Ma, Hongbo Yang, Wen Xidian Univ Sch Mechanoelect Engn Xian 710071 Shaanxi Peoples R China Jiangsu Jinxiang Transmiss Equipment Co Ltd Huaian 223001 Peoples R China

A multi-ensemble method is proposed based on deep auto-encoder (DAE) for fault diagnosis of rolling bearings. At first, several DAEs with different activation functions are trained to obtain different types of features, which are merged into a feature pool. Then, the features in the feature pool are evaluated and selected. The classifiers are constructed for each feature. The classification accuracy is used as evaluation index and good features are selected. Finally, the train data is cross-divided and multi sample-sets with selected features are constructed. Each sample-set is used to train a classifier, the final diagnosis result is obtained by majority voting among these classification results. The proposed method is validated by two experimental bearing vibrations signals and also compared with other methods. The results revealed that different types of features can be obtained by DAEs with different activation functions. The proposed method has a high accuracy and good generalization ability. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Rolling bearing Fault diagnosis Ensemble strategy deep auto-encoder Multi-ensemble

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A new subset based deep feature learning method for intelligent fault diagnosis of bearing

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EXPERT SYSTEMS WITH APPLICATIONS 2018年 110卷 125-142页

作者： Zhang, Yuyan Li, Xinyu Gao, Liang Li, Peigen Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China

Intelligent fault diagnosis has attracted considerable attention due to its ability in effectively processing massive data and rapidly providing diagnosis results. However, in the traditional intelligent diagnosis methods of bearing, features are extracted manually. Such process is not only a grueling and time-consuming work but also greatly affects the diagnosis results. In this study, we propose a new intelligent diagnosis method of bearing, which can learn features automatically. First, a new subset approach is developed and it is helpful to learn the discriminative features from different fault patterns. Second, a subset based deep auto-encoder (SBTDA) model is proposed to realize the automatic feature extraction. Additionally, a new self-adaptive fine-tuning operation is designed to ensure the good convergence performance of SBTDA. Finally, to obtain the appropriate configuration, several key parameters are optimized with particle swarm optimization algorithm. The proposed method is evaluated on three public bearing datasets, and achieves the average testing accuracies of 99.65%, 99.66% and 99.60% respectively. The comparisons with 13 intelligent diagnosis methods demonstrate that SBTDA can obtain higher diagnosis accuracy. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Bearing intelligent fault diagnosis deep feature learning Subset approach deep auto-encoder Particle swarm optimization

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A Method for Guaranteeing Wireless Communication Based on a Combination of deep and Shallow Learning

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IEEE ACCESS 2019年 7卷 38688-38695页

作者： Tian, Qiao Li, Jingmei Liu, Haibo Harbin Engn Univ Coll Comp Sci & Technol Harbin 150001 Heilongjiang Peoples R China

Wireless communication has changed and improved people's lives and society, especially with the arrival of the Internet of Things (IoT) era. Despite the maturity of wireless communication, the security issue of communication remains the most stubborn and troublesome problem due to the increasingly complex and large amounts of data. An intrusion detection system is the guarantee of secure communication. However, variable protocols and drastic growth in data volume make intrusion detection a difficult task. In this paper, we proposed a framework of anomaly-based network intrusion detection system to finish the detection job. First, UNSW-NB15 is selected as the research object. Based on this new dataset, we built a detection model combining a deep learning method and a shallow learning approach. The former one is a deep auto-encoder used for feature learning, which can discover important representations of data and accelerate detection. The latter one is a powerful support vector machine (SVM), where the artificial bee colony (ABC) algorithm is used to find optimal parameters for SVM with five-fold cross validation (5FCV). Various experiments are conducted and the simulation results prove that the proposed method performs quite better than some of state-of-the-art intrusion detection approaches, including the method based on the principal component analysis (PCA) and some other machine learning strategies.

关键词： Wireless communication big data intrusion detection system deep auto-encoder

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deep Hashing and Sparse Representation of Abnormal Events Detection

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COMPUTER JOURNAL 2024年第1期67卷 3-17页

作者： Gnouma, Mariem Ejbali, Ridha Zaied, Mourad Univ Gabes Natl Engn Sch Gabes Res Team Intelligent Machines St Omar Ibn Khattab Zrig Eddakhlania Gabes 6029 Tunisia Fac Sci Gabes Dept Comp Sci Gabes 6072 Tunisia

Due to its widespread application in the field of public security, anomaly detection in crowd scenes has recently become a hot topic. Some deep learning-based methods led to significant accomplishments in this field. Nevertheless, due to the scarcity of data and the misclassification of queries which most of them suffer to some extent from a sudden and infrequent overfitting. Though, we tried to solve the above problems, understand the long video streams and establish an accurate and reliable security system in order to improve its performance in detecting anomalies. We also referred to the hash technique, which has proven to be the most efficient method used when researching about large-scale image recovery. Thus, this article offers a smart video anomaly detection solution. In this paper, we combine the advantages of both deep hashing and deep auto-encoders to show that tracking changes in deep hash components across time and can be used to detect local anomalies. More precisely, we start with a new technique to minimize the mass of input data and information in order to decrease the time of calculation using a new dynamic frame skipping technique. Then, we propose to measure local anomalies by combining semantic with low-level optical flows to balance the performance and perceptibility. The experimental results illustrate that the proposed methods surpass these baselines for the detection and localization of anomalies.

关键词： abnormal detection optical flow deep auto-encoder hashing code

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Robust deep auto-encoding Gaussian process regression for unsupervised anomaly detection

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NEUROCOMPUTING 2020年 376卷 180-190页

作者： Fan, Jinan Zhang, Qianru Zhu, Jialei Zhang, Meng Yang, Zhou Cao, Hanxiang Southeast Univ Natl ASIC Syst Engn Technol Res Ctr Nanjing Jiangsu Peoples R China

Unsupervised anomaly detection (AD) is of great importance in both fundamental machine learning researches and industrial applications. Previous approaches have achieved great advance in improving the performance of unsupervised AD model recently. However, there are still some thorny issues unsolved, especially the problem of efficiency degradation when dealing with high-dimensional data and the inability to maintain robustness when dealing with contaminated data, which have not been addressed simultaneously in the existing models. In our work, we propose a novel hybrid unsupervised AD method, which first integrates convolutional auto-encoder and Gaussian process regression to extract features and to remove anomalies from noisy data as well. Our model behaves more effectively at modeling high-dimension data and more robust to variation of the anomaly rate in dataset. We evaluate its performance on four publicly benchmark datasets and show the state-of-the-art performance against competitive methods. (C) 2019 Published by Elsevier B.V.

关键词： Anomaly detection deep auto-encoder Gaussian process High-dimension data Data contamination

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Extracting Spectral Features Using deep autoencoders With Binary Distributed Hidden Units for Statistical Parametric Speech Synthesis

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2018年第4期26卷 713-724页

作者： Hu, Ya-Jun Ling, Zhen-Hua Univ Sci & Technol China Natl Engn Lab Speech & Language Informat Proc Hefei 230027 Anhui Peoples R China

This paper presents a spectral feature extraction method using deep autoencoders (DAEs) with binary distributed hidden units (BDAE) for statistical parametric speech synthesis (SPSS). Conventional DAEs are trained to minimize the error of reconstructing raw features. In this paper, we investigate another important property of DAEs that may influence their performances as feature extractors for regression tasks, i.e., the degree of binarization of hidden units. Our analysis shows that making the hidden units ofDAEs to be binary may help alleviate the over-smoothing effect caused by acoustic modeling and parameter generation, which are one of the main deficiencies of current SPSS systems. This paper further proposes an effective BDAE training method by adding noise to the input of hidden units during model training and applying DBN-based pretraining strategies. Our experiments adopt feedforward deep neural networks as acoustic models for SPSS and compare the performances of different spectral feature extractors. Experimental results show that when extracting low-dimensional spectral features by BDAEs, the predicted spectral features can reconstruct spectral envelopes closer to natural samples than using conventional DAEs. Subjective evaluations on the synthetic voices of a Chinese speaker and an English speaker demonstrate that BDAEs achieve better naturalness of synthetic speech than conventional mel-cepstra and other neural network based feature extractors, such as DAEs and DBNs.

关键词： deep auto-encoder deep belief network binary distributed hidden units speech synthesis

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FunkR-pDAE: Personalized Project Recommendation Using deep Learning

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 2021年第2期9卷 886-900页

作者： Zhang, Pengcheng Xiong, Fang Leung, Hareton Song, Wei Hohai Univ Coll Comp & Informat Nanjing 211100 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China

In open source communities, developers always need to spend plenty of time and energy on discovering specific projects from massive open source projects. Consequently, the study of personalized project recommendation for developers has important theoretical and practical significance. However, existing recommendation approaches have clear limitations, such as ignoring developers' operating behavior, social relationships and practical skills, and are very inefficient for large amounts of data. To address these limitations, this paper proposes FunkR-pDAE (Funk singular value decomposition Recommendation using pearson correlation coefficient and deep auto-encoders), a novel personalized project recommendation approach using a deep learning model. FunkR-pDAE first extracts data related to developers and open source projects from open source communities, which build a developer-open source project relevance matrix and a developer-developer relevance matrix. Meanwhile, Pearson Correlation Coefficient is utilized to calculate developer similarity using the developer-developer relevance matrix. Second, deep auto-encoders are used to learn the factor vectors that represent developers and open source projects. Finally, a sorting method is defined to provide personalized project recommendations. Experimental results on real-world GitHub data sets show that FunkR-pDAE has a precision rate of 75.46 percent and a recall rate of 40.32 percent, which provides more effective recommendation compared with state-of-the-art approaches.

关键词： Open source project recommendation deep auto-encoder GitHub

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Single-cell RNA-seq data clustering by deep information fusion

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BRIEFINGS IN FUNCTIONAL GENOMICS 2024年第2期23卷 128-137页

作者： Ren, Liangrui Wang, Jun Li, Wei Guo, Maozu Yu, Guoxian Shandong Univ Sch Software Jinan 250101 Peoples R China Shandong Univ Sch Software Jinan Peoples R China Shandong Univ Joint SDU NTU Ctr Artificial Intelligence Res C FA Jinan Peoples R China Shandong Univ Sch Control Sci & Engn Jinan Peoples R China Beijing Univ Civil Engn & Architecture Sch Elect & Informat Engn Beijing Peoples R China

Determining cell types by single-cell transcriptomics data is fundamental for downstream analysis. However, cell clustering and data imputation still face the computation challenges, due to the high dropout rate, sparsity and dimensionality of single-cell data. Although some deep learning based solutions have been proposed to handle these challenges, they still can not leverage gene attribute information and cell topology in a sensible way to explore the consistent clustering. In this paper, we present scdeepFC, a deep information fusion-based single-cell data clustering method for cell clustering and data imputation. Specifically, scdeepFC uses a deep auto-encoder (DAE) network and a deep graph convolution network to embed high-dimensional gene attribute information and high-order cell-cell topological information into different low-dimensional representations, and then fuses them to generate a more comprehensive and accurate consensus representation via a deep information fusion network. In addition, scdeepFC integrates the zero-inflated negative binomial (ZINB) into DAE to model the dropout events. By jointly optimizing the ZINB loss and cell graph reconstruction loss, scdeepFC generates a salient embedding representation for clustering cells and imputing missing data. Extensive experiments on real single-cell datasets prove that scdeepFC outperforms other popular single-cell analysis methods. Both the gene attribute and cell topology information can improve the cell clustering.

关键词： single-cell RNA-seq clustering graph convolution network deep auto-encoder ZINB transcriptomics

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Compression by and for deep Boltzmann Machines

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IEEE TRANSACTIONS ON COMMUNICATIONS 2020年第12期68卷 7498-7510页

作者： Li, Qing Chen, Yang Kim, Yongjune Western Digital Res Milpitas CA 95035 USA Univ Michigan Dept Stat Ann Arbor MI 48109 USA Univ Michigan Michigan Inst Data Sci Ann Arbor MI 48109 USA Daegu Gyeongbuk Inst Sci & Technol DGIST Dept Informat & Commun Engn Daegu 42988 South Korea

We answer two questions in this work: what deep Boltzmann Machines (DBMs) can do for compression and vise versa. We show that (1) DBMs can be applied to learn the rate distortion approaching posterior as in the Blahut-Arimoto (BA) algorithm, and to construct a lossy source compression scheme based on the deep autoencoder;(2) compression can improve DBMs' training performances via compression-based denoising algorithms. The implementation of the BA algorithm in the form of DBMs is the foundation of the two applications.

关键词： Image coding Rate-distortion Noise reduction Machine learning Training Distortion Source coding Rate distortion lossy source coding deep Boltzmann machines deep auto-encoder Blahut-Arimoto algorithm denoising

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