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检索条件"主题词=Stacked denoising autoencoder"

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The research of underwater target recognition method based on deep learning 7

The research of underwater target recognition method based o...

引用

IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)

作者： Chen, Yuechao Xu, Xiaonan Hangzhou Appl Acoust Res Inst Natl Key Lab Sci & Technol Sonar Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781538631423

The deep learning is a popular research direction in machine learning field now. In this paper, the deep learning algorithms are used to recognize the underwater target radiated noises. The deep belief network (DBN) model and the stacked denoising autoencoder (SDAE) model are built respectively. Then the underwater acoustic simulated data of different types of targets as well as different states of one target and experimental data of different states of one target were recognized by these models, and the support vector machine (SVM), general regression neural network (GRNN) and probabilistic neural network (PNN) are selected as the comparison algorithms. The spectrum features extracted from the target radiated noises are used as the input data of the recognition models. The results show that the recognition accuracy of DBN and that of SDAE are all higher than that of the other three methods for all situations, which shows that the deep learning algorithms can effectively improve the underwater target recognition effect.

关键词： Deep belief network stacked denoising autoencoder Underwater target recognition Spectrum feature

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A Semi-supervised stacked autoencoder Approach for Network Traffic Classification 28

A Semi-supervised Stacked Autoencoder Approach for Network T...

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28th IEEE International Conference on Network Protocols (IEEE ICNP)

作者： Aouedi, Ons Piamrat, Kandaraj Bagadthey, Dhruvjyoti Univ Nantes LS2N 2 Chemin Houssiniere Nantes France IIT Madras Dept Elect Engn Chennai 600036 Tamil Nadu India

ISBN: (纸本)9781728169927

Network traffic classification is an important task in modern communications. Several approaches have been proposed to improve the performance of differentiating among applications. However, most of them are based on supervised learning where only labeled data are used. In reality, a lot of datasets are partially labeled due to many reasons and unlabeled portions of the data, which can also provide informative characteristics, are ignored. To handle this issue, we propose a semi-supervised approach based on deep learning. We deployed deep learning because of its unique nature for solving problems, and its ability to take into account both labeled and unlabeled data. Moreover, it can also integrate feature extraction and classification into a single model. To achieve these goals, we propose an approach using stacked sparse autoencoder (SSAE) accompanied by denoising and dropout techniques to improve the robustness of extracted features and prevent the over-fitting problem during the training process. The obtained results demonstrate a better performance than traditional models while keeping the whole procedure automated.

关键词： Traffic classification Feature extraction Deep learning Machine learning stacked autoencoder stacked denoising autoencoder Dropout Semi-supervised learning

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Collaborative Filtering Algorithm Based on SDAE and Time Mean Model 8

Collaborative Filtering Algorithm Based on SDAE and Time Mea...

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8th International Conference on Advanced Cloud and Big Data (CBD)

作者： Sun, Jingyu Ning, Zefei Wang, Cong Wang, Xinjuan Taiyuan Univ Technol Coll Software Taiyuan Peoples R China Beijing Smartchip Microelect Technol Co Ltd Secur & Sensing Business Unit Beijing Peoples R China Taiyuan Univ Technol Coll Informat & Comp Taiyuan Peoples R China

ISBN: (纸本)9781665423137

Aiming at solving the problem of data sparsity and ignorance of user interest drift in traditional collaborative filtering recommendation, this paper propose a novel collaborative filtering algorithm by combing the automatic feature extraction capability of stacked denoising autoencoder (SDAE) and the ability of real-time recommendation and rapid processing of big data of time mean model (TMM). Time factor of ratings are fully considered, and the proposed model can effectively alleviate the information distortion in feature extraction of traditional recommendation algorithms. Experimental results show that SDAE-TMM can greatly improve the performance of real-time recommendation system.

关键词： recommender system collaborative filtering sparse data stacked denoising autoencoder time mean model

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Hybrid Deep Neural Network based on SDAE and GRUNN 39

Hybrid Deep Neural Network based on SDAE and GRUNN

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39th Chinese Control Conference (CCC)

作者： Zou, Yingyong Yu, Jun Tang, Jiangen Zhang, Yongde Harbin Univ Sci & Technol Sch Mech & Power Engn Harbin 150080 Peoples R China Changchun Univ Coll Mech & Vehicular Engn Changchun 130022 Peoples R China Harbin Univ Sci & Technol Key Lab Adv Mfg & Intelligent Technol Harbin 150080 Peoples R China Harbin Univ Sci & Technol Sch Automat Harbin 150080 Peoples R China

ISBN: (纸本)9789881563903

stacked autoencoder (SAE) is hard to achieve satisfactory performance, when input data are complex and non-stationary. Besides, the identification performance of recurrent neural network (RNN) may decrease rapidly under noisy environment. In order to deal with these problems, a novel hybrid deep neural network (DNN) based on stacked denoising autoencoder (SDAE) and gated recurrent unit neural network (GRUNN) is presented. First, the structure of the presented hybrid DNN is given. The hybrid DNN contains a SDAE, a GRUNN, and a softmax classifier. Then, the training algorithm based on action discovery (AD) is proposed to train the presented hybrid DNN. The experimental studies indicate the presented hybrid DNN processes strong anti-noise ability and adaptability to time-varying signals.

关键词： deep neural network stacked denoising autoencoder gated recurrent unit neural network denoising ability action discovery

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An ELM-based Deep SDAE Ensemble for Inter-Subject Cognitive Workload Estimation with Physiological Signals 39

An ELM-based Deep SDAE Ensemble for Inter-Subject Cognitive ...

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39th Chinese Control Conference (CCC)

作者： Zheng, Zhanpeng Yin, Zhong Zhang, Jianhua Univ Shanghai Sci & Technol Engn Res Ctr Opt Instrument & Syst Minist Educ Shanghai Key Lab Modern Opt Syst Shanghai 200093 Peoples R China Oslo Metropolitan Univ Dept Comp Sci OsloMet Artificial Intelligence Lab N-0130 Oslo Norway

ISBN: (数字)9789881563903

ISBN: (纸本)9789881563903

Evaluating operator cognitive workload (CW) levels in human-machine systems based on neurophysiological signals is becoming the basis to prevent serious accidents due to abnormal state of human operators. This study proposes an inter-subject CW classifier, extreme learning machine (ELM)-based deep stacked denoising autoencoder ensemble (ED-SDAE), to adapt the variations of the electroencephalogram (EEG) feature distributions across different subjects. The ED-SDAE consists of two cascade-connected modules, which are termed as high level personalized feature abstractions and abstraction fusion. The combination of SDAE and locality preserving projection (LPP) technique is regarded as base learner to obtain ensemble members for training meta-classifier by stacking-based approach. The ELM model with Q-statistics diversity measurement is acted as meta-classifier to fuse above inputs to improve classification performance. The feasibility of the SD-SDAE is tested by two EEG databases. The multi-class classification rate achieves 0.6353 and 0.6747 for TI and T2 respectively, and significantly outperforms several shallow and deep CW estimators. By computing the main time complexity, the computational workload of the ED-SDAE is also acceptable for high-dimensional EEG features.

关键词： Cognitive workload EEG ensemble learning extreme learning machine stacked denoising autoencoder

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Intrusion Detection Algorithm Based on SDA-ELM 1

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34th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE)

作者： Wei, Xiaotao Ren, Shuyu Li, Yinglong Wang, Xi-Xi Jin, Mengxia Beijing Jiaotong Univ Sch Software Beijing Peoples R China

ISBN: (数字)9783030794637

ISBN: (纸本)9783030794620;9783030794637

A deep learning intrusion detection algorithm based on stacked denoising autoencoder and extreme learning machine (SDA-ELM) is proposed to solve the problem that the traditional machine learning algorithm can't cope with the classification of multisource heterogeneous network intrusion data. The algorithm use Dropout regularization to improve the SDA deep learning model, and the integration features of low dimensionality and high robustness are extracted. Then the model use ELM to carry out a supervised learning for the low dimension data to recognize the network attack. The algorithm combines the abstract feature extraction capability of SDA and the fast learning ability of ELM. The experimental results show that the SDA-ELM algorithm improves the accuracy of classification and the detection rate of small sample attacks, reduces the false alarm rate.

关键词： Intrusion detection stacked denoising autoencoder Extreme learning machine

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Classification of EEG Signals for Cognitive Load Estimation Using Deep Learning Architectures 10th

Classification of EEG Signals for Cognitive Load Estimation ...

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10th International Conference on Intelligent Human Computer Interaction (IHCI)

作者： Saha, Anushri Minz, Vikash Bonela, Sanjith Sreeja, S. R. Chowdhury, Ritwika Samanta, Debasis Indian Inst Technol Kharagpur Dept Comp Sci & Engn Kharagpur W Bengal India Indian Inst Technol Kharagpur Dept Elect & Elect Commun Engn Kharagpur W Bengal India

ISBN: (纸本)9783030040215;9783030040208

Measuring cognitive load is crucial for many applications such as information personalization, adaptive intelligent tutoring systems, etc. Cognitive load estimation using Electroencephalogram (EEG) signals is widespread as it produces clear indications of cognitive activities by measuring changes of neural activation in the brain. However, the existing cognitive load estimation techniques are based on machine learning algorithms, which follow signal denoising and hand-crafted feature extraction to classify different loads. There is a need to find a better alternative to the machine learning approach. Of late, deep learning approach has been successfully applied to many applications namely, computer vision, pattern recognition, speech processing, etc. However, deep learning has not been extensively studied for the classification of cognitive load data captured by an EEG. In this work, two deep learning models are studied, namely stacked denoising autoencoder (SDAE) followed by a multilayer perceptron (MLP) and long short term memory (LSTM) followed by an MLP to classify cognitive load data. SDAE and LSTM are used for feature extraction and MLP for classification. It is observed that deep learning models perform significantly better than the conventional machine learning classifiers such as support vector machine (SVM), k-nearest neighbors (KNN), and linear discriminant analysis (LDA).

关键词： Cognitive load stacked denoising autoencoder Long short term memory Multilayer perceptron

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A DEEP LEARNING APPROACH FOR CANCER DETECTION AND RELEVANT GENE IDENTIFICATION 22nd

A DEEP LEARNING APPROACH FOR CANCER DETECTION AND RELEVANT G...

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22nd Pacific Symposium on Biocomputing (PSB)

作者： Danaee, Padideh Ghaeini, Reza Hendrix, David A. Oregon State Univ Sch Elect Engn & Comp Sci Corvallis OR 97330 USA Oregon State Univ Sch Elect Engn & Comp Sci Dept Biochem & Biophys Corvallis OR 97330 USA

ISBN: (纸本)9789813207813;9789813207806

Cancer detection from gene expression data continues to pose a challenge due to the high dimensionality and complexity of these data. After decades of research there is still uncertainty in the clinical diagnosis of cancer and the identification of tumor-specific markers. Here we present a deep learning approach to cancer detection, and to the identification of genes critical for the diagnosis of breast cancer. First, we used stacked denoising autoencoder (SDAE) to deeply extract functional features from high dimensional gene expression profiles. Next, we evaluated the performance of the extracted representation through supervised classification models to verify the usefulness of the new features in cancer detection. Lastly, we identified a set of highly interactive genes by analyzing the SDAE connectivity matrices. Our results and analysis illustrate that these highly interactive genes could be useful cancer biomarkers for the detection of breast cancer that deserve further studies.

关键词： Cancer Detection RNA-seq Expression Deep Learning Dimensionality Reduction stacked denoising autoencoder Classification

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Visual Object Tracking via Deep Neural Network 1

Visual Object Tracking via Deep Neural Network

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IEEE First International Smart Cities Conference (ISC2)

作者： Xu, Tianyang Wu, Xiaojun Jiangnan Univ Sch IoT Engn Wuxi 214122 Peoples R China

ISBN: (纸本)9781467365529

Visual tracking is a fundamental research problem in computer vision field. In this paper, we propose an approach to incorporate visual prior into visual object tracking via deep neural network. Visual prior knowledge is expressed as the parameters of a stacked denoising autoencoder, which is trained from a large collection of natural images. By utilizing natural images, we can obtain generic image features which are more robust against variations. Then we design a classifier for tracking using the same structure as the stacked denoising autoencoder, tracking is then carried out under a particle filter framework by determining the current target's location and updating the parameters. In addition, in order to alleviate the computational burden caused by deep structure, an adaptive updating mechanism is proposed. As a result, we apply a general-to-special strategy for our stacked denoising autoencoder tracker (SDAT), the learned visual prior provides a reasonable initial value for parameters of the neural network, and the deep structure of our tracker is robust to appearance variations. Experiments over 50 challenging videos indicate the effectiveness and robustness of our tracker, and the resulting tracker is outstanding especially against variations with the existing state-of-the-art methods.

关键词： Object tracking visual prior stacked denoising autoencoder

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Citation Recommendation Based on Knowledge Graph and Multi-task Learning 16th

Citation Recommendation Based on Knowledge Graph and Multi-t...

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16th International Conference on Knowledge Science, Engineering and Management (KSEM)

作者： Wan, Jing Yuan, Minghui Wang, Danya Fu, Yao Beijing Univ Chem Technol Beijing 100029 Peoples R China

ISBN: (纸本)9783031402883;9783031402890

The Citation Recommendation aims to address the problem of academic information overload by filtering and suggesting relevant references for researchers. Traditional content-based citation recommendation methods may not be comprehensive enough to extract paper attributes that are essential for evaluating paper content similarity. To better use the abundant attributes and interaction information, the knowledge graph is introduced to recommendation system recently. We construct a multi-task learning-based model for citation recommendation that incorporates a knowledge graph, consisting of two primary tasks: citation recommendation and knowledge graph link prediction. To identify the interactions between papers, we propose a pseudointeraction matrix in the citation recommendation task. The knowledge graph link prediction task aids in identifying paper attribute information and enhancing representation. By automatically merging and sharing low-level features, exploring feature similarity, and enhancing the performance of both tasks, the multi-task learning framework can improve the final recommendation result significantly. Multiple experiments on the academic paper datasets AMiner and DBLP verify the effectiveness of our proposed model.

关键词： Citation recommendation Knowledge graph Multi-task learning stacked denoising autoencoder Link prediction

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