Search Results - "sparse autoencoder"

EndNet: Sparse AutoEncoder Network for Endmember Extraction and Hyperspectral Unmixing by Ozkan, Savas, Kaya, Berk, Akar, Gozde Bozdagi

“…Data acquired from multichannel sensors are a highly valuable asset to interpret the environment for a variety of remote sensing applications. However, low…”
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Group sparse autoencoder by Sankaran, Anush, Vatsa, Mayank, Singh, Richa, Majumdar, Angshul

“… In this research, we consider autoencoder as the feature learning architecture and propose ℓ2,1-norm based regularization to improve its learning capacity, called as Group Sparse AutoEncoder (GSAE…”
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Unsupervised Spectral-Spatial Feature Learning With Stacked Sparse Autoencoder for Hyperspectral Imagery Classification by Tao, Chao, Pan, Hongbo, Li, Yansheng, Zou, Zhengrou

“… This is achieved by learning a feature mapping function based on stacked sparse autoencoder. Considering that hyperspectral imagery (HSI…”
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Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images by Jun Xu, Lei Xiang, Qingshan Liu, Gilmore, Hannah, Jianzhong Wu, Jinghai Tang, Madabhushi, Anant

“…Automated nuclear detection is a critical step for a number of computer assisted pathology related image analysis algorithms such as for automated grading of…”
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Deep Transfer Learning Based on Sparse Autoencoder for Remaining Useful Life Prediction of Tool in Manufacturing by Sun, Chuang, Ma, Meng, Zhao, Zhibin, Tian, Shaohua, Yan, Ruqiang, Chen, Xuefeng

“… In this paper, a deep transfer learning (DTL) network based on sparse autoencoder (SAE) is presented. In the DTL method, three transfer strategies, that is, weight transfer…”
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Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network by Chen, Zhuyun, Li, Weihua

“… First, time-domain and frequency-domain features are extracted from the different sensor signals, and then these features are input into multiple two-layer sparse autoencoder (SAE…”
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Denoising Sparse Autoencoder-Based Ictal EEG Classification by Qiu, Yang, Zhou, Weidong, Yu, Nana, Du, Peidong

“… This paper presents an EEG classification framework based on the denoising sparse autoencoder…”
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EEG-Based Emotion Classification Using a Deep Neural Network and Sparse Autoencoder by Liu, Junxiu, Wu, Guopei, Luo, Yuling, Qiu, Senhui, Yang, Su, Li, Wei, Bi, Yifei

“…), Sparse Autoencoder (SAE), and Deep Neural Network (DNN) together. In the proposed network, the features extracted by the CNN are first sent to SAE for encoding and decoding…”
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GSAMDA: a computational model for predicting potential microbe–drug associations based on graph attention network and sparse autoencoder by Tan, Yaqin, Zou, Juan, Kuang, Linai, Wang, Xiangyi, Zeng, Bin, Zhang, Zhen, Wang, Lei

“… Results In this work, we proposed a novel computational model named GSAMDA based on the graph attention network and sparse autoencoder to infer latent microbe–drug associations…”
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GRU‐based stacked sparse autoencoder with attention mechanism for process monitoring by Miao, Zengdi, Wu, Ping, Wu, Zhenquan, Jia, Xiangjun, Xu, Hui, Jiang, Jian

“… In this work, a gated recurrent unit‐based stacked sparse autoencoder with attention mechanism (GRU‐SSAE‐AM…”
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Softmax regression based deep sparse autoencoder network for facial emotion recognition in human-robot interaction by Chen, Luefeng, Zhou, Mengtian, Su, Wanjuan, Wu, Min, She, Jinhua, Hirota, Kaoru

“… However, DNN suffers from problems of learning efficiency and computational complexity. To address these problems, deep sparse autoencoder network (DSAN…”
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Deep Learning Approach Combining Sparse Autoencoder With SVM for Network Intrusion Detection by Al-Qatf, Majjed, Lasheng, Yu, Al-Habib, Mohammed, Al-Sabahi, Kamal

“…Network intrusion detection systems (NIDSs) provide a better solution to network security than other traditional network defense technologies, such as firewall…”
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A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction by Huang, Faming, Zhang, Jing, Zhou, Chuangbing, Wang, Yuhao, Huang, Jinsong, Zhu, Li

“…The environmental factors of landslide susceptibility are generally uncorrelated or non-linearly correlated, resulting in the limited prediction performances…”
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Sparse autoencoder for unsupervised nucleus detection and representation in histopathology images by Hou, Le, Nguyen, Vu, Kanevsky, Ariel B., Samaras, Dimitris, Kurc, Tahsin M., Zhao, Tianhao, Gupta, Rajarsi R., Gao, Yi, Chen, Wenjin, Foran, David, Saltz, Joel H.

“…We propose a sparse Convolutional Autoencoder (CAE) for simultaneous nucleus detection and feature extraction in histopathology tissue images. Our CAE detects…”
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A hybrid Intrusion Detection System based on Sparse autoencoder and Deep Neural Network by Narayana Rao, K., Venkata Rao, K., P.V.G.D., Prasad Reddy

“… In the first stage, the unsupervised Sparse autoencoder (SAE) with smoothed l1 regularization…”
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Detection and Classification of Transmission Line Faults Based on Unsupervised Feature Learning and Convolutional Sparse Autoencoder by Chen, Kunjin, Hu, Jun, He, Jinliang

“…We present in this paper a novel method for fault detection and classification in power transmission lines based on convolutional sparse autoencoder…”
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Time Series Prediction Using Sparse Autoencoder and High-Order Fuzzy Cognitive Maps by Wu, Kai, Liu, Jing, Liu, Penghui, Yang, Shanchao

“…The problem of time series prediction based on fuzzy cognitive maps (FCMs) is unresolved. Although many methods have been proposed to cope with this issue, the…”
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A Fuzzy Deep Neural Network With Sparse Autoencoder for Emotional Intention Understanding in Human-Robot Interaction by Chen, Luefeng, Su, Wanjuan, Wu, Min, Pedrycz, Witold, Hirota, Kaoru

“…A fuzzy deep neural network with sparse autoencoder (FDNNSA) is proposed for intention understanding based on human emotions and identification information (i.e…”
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Combustion stability monitoring through flame imaging and stacked sparse autoencoder based deep neural network by Han, Zhezhe, Hossain, Md. Moinul, Wang, Yuwei, Li, Jian, Xu, Chuanlong

“…•A novel deep learning model is established for predicting combustion stability.•Automatic generation of combustion stability label is achieved.•Quantitative…”
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Self-adaptive teaching-learning-based optimizer with improved RBF and sparse autoencoder for high-dimensional problems by Bi, Jing, Wang, Ziqi, Yuan, Haitao, Zhang, Jia, Zhou, MengChu

“… them may easily trap into local optima. To solve this problem, this work proposes a Self-adaptive Teaching-learning-based Optimizer with an improved Radial basis function model and a sparse Autoencoder (STORA…”
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