Search Results - Deep autoencoder Gaussian mixture model

Unsupervised detection of multivariate geochemical anomalies using a high-performance deep autoencoder Gaussian mixture model by Wang, Xuemei, Chen, Yongliang

“…It is of great significance to construct an efficient geochemical anomaly detection model for the successful accomplishment of a mineral exploration process in a complex geological environment…”
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Aircraft Trajectory Clustering in Terminal Airspace Based on Deep Autoencoder and Gaussian Mixture Model by Zeng, Weili, Xu, Zhengfeng, Cai, Zhipeng, Chu, Xiao, Lu, Xiaobo

“… Therefore, this paper mainly tries a deep trajectory clustering method based on deep autoencoder (DAE…”
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scGMAI: a Gaussian mixture model for clustering single-cell RNA-Seq data based on deep autoencoder by Yu, Bin, Chen, Chen, Qi, Ren, Zheng, Ruiqing, Skillman-Lawrence, Patrick J, Wang, Xiaolin, Ma, Anjun, Gu, Haiming

“… Here, we propose the scGMAI, which is a new single-cell Gaussian mixture clustering method based on autoencoder networks and the fast independent component analysis (FastICA…”
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Video anomaly detection and localization via Gaussian Mixture Fully Convolutional Variational Autoencoder by Fan, Yaxiang, Wen, Gongjian, Li, Deren, Qiu, Shaohua, Levine, Martin D., Xiao, Fei

“…), while anomalies either do not belong to any Gaussian component. The method is based on Gaussian Mixture Variational Autoencoder, which can learn feature representations of the normal samples as a Gaussian Mixture Model trained using deep learning…”
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Denoising Deep Autoencoder Gaussian Mixture Model and Its Application for Robust Nonlinear Industrial Process Monitoring by Zhou, Yin-Chang, Li, Meng-Qian, Ji, Long-Bin

“… This paper presents a denoising deep autoencoder Gaussian mixture model (DDAGMM) for anomaly detection in the industrial process…”
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An Unsupervised Self-Organizing Map Assisted Deep Autoencoder Gaussian Mixture Model for IoT Anomaly Detection by Saha, Kristy, Rahman Fakir, Md. Mushfiqur, Hashem, M. M. A

“… We propose an unsupervised self-organizing map-assisted deep autoencoder Gaussian mixture model…”
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Unsupervised Real-Time Communication Traffic Anomaly Detection for Multi-Dimensional Industrial Networks by hao, Weijie, Zhang, Zebang, Wang, Xuan, Yang, Qiang, Liu, Bo, Wang, Wenhai, Yang, Tao, Ye, Peng

“… The deep autoencoder Gaussian mixture model (DAGMM) is employed and fine-tuned accordingly to generate normal behavior patterns with high-dimensional, large-scale traffic data considering the real-time response of the detection system…”
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MAGMM: A high-dimensional outlier detection algorithm based on a memory-augmented autoencoder and the Gaussian mixture model by Zhang, Zhongping, Wang, Kuo, Wang, Zhongman, Li, Junji

“… outlier detection algorithm based on a memory-augmented autoencoder and the Gaussian mixture model…”
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Deep unsupervised clustering by information maximization on Gaussian mixture autoencoders by Wu, Peng, Pan, Li

“… In this paper, we propose the Gaussian Mixture Autoencoder (GMAE), a deep clustering method that integrates a probabilistic Autoencoder (AE…”
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Deep Clustering by Gaussian Mixture Variational Autoencoders With Graph Embedding by Yang, Linxiao, Cheung, Ngai-Man, Li, Jiaying, Fang, Jun

“…We propose DGG: Deep clustering via a Gaussian-mixture variational autoencoder (VAE) with Graph embedding…”
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Detection of Health Data Based on Gaussian Mixture Generative Model by ZHU Zhuangzhuang, ZHOU Zhiping

“… Aiming at the problem, a method of detecting health data is utilized, called Gaussian mixture generative model (GMGM…”
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Deep convolutional neural network-based anomaly detection for organ classification in gastric X-ray examination by Togo, Ren, Watanabe, Haruna, Ogawa, Takahiro, Haseyama, Miki

“… We constructed a deep autoencoding gaussian mixture model (DAGMM) with a convolutional autoencoder architecture…”
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scGMM-VGAE: a Gaussian mixture model-based variational graph autoencoder algorithm for clustering single-cell RNA-seq data by Lin, Eric, Liu, Boyuan, Lac, Leann, Fung, Daryl L X, Leung, Carson K, Hu, Pingzhao

“… In this study, we propose a Gaussian mixture model-based variational graph autoencoder on scRNA-seq data (scGMM-VGAE…”
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Deep federated learning hybrid optimization model based on encrypted aligned data by Zhao, Zhongnan, Liang, Xiaoliang, Huang, Hai, Wang, Kun

“…•Improving the quality of Federal Learning encrypted alignment data.•Use Gaussian mixture clustering to cluster samples and set a threshold to filter samples…”
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Steam turbine anomaly detection: an unsupervised learning approach using enhanced long short-term memory variational autoencoder by Xu, Weiming, Zhang, Peng

“… This study proposes an Enhanced Long Short-Term Memory Variational Autoencoder with Deep Advanced Features and Gaussian Mixture Model (ELSTMVAE-DAF-GMM…”
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Geochemical anomaly recognition based on Gaussian mixture model by Wang, Haijun, Xue, Linfu, Ran, Xiangjin

“…In order to find the optimal method for identifying geochemical anomalies in geochemical exploration, the Gaussian mixture model and the deep autoencoder network were compared in this paper…”
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Hybrid Deep Learning Framework for Anomaly Detection in Power Plant Systems by Wang, Shuchong, Zhao, Changxiang, Liu, Xingchen, Ni, Xianghong, Chen, Xu, Gao, Xinglong, Sun, Li

“…, which combines the deep autoencoder (DAE), Transformer, and Gaussian mixture model (GMM) to establish an anomaly detection model…”
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Deep learning model to identify and validate hypotension endotypes in surgical and critically ill patients by Jian, Zhongping, Liu, Xianfu, Kouz, Karim, Settels, Jos J, Davies, Simon, Scheeren, Thomas W L, Fleming, Neal W, Veelo, Denise P, Vlaar, Alexander P J, Sander, Michael, Cannesson, Maxime, Berger, David, Pinsky, Michael R, Sessler, Daniel I, Hatib, Feras, Saugel, Bernd

“… We developed an unsupervised deep learning algorithm, specifically a deep learning autoencoder model combined with a Gaussian mixture model, to identify endotypes of hypotension based on stroke…”
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Anomaly Detection in Electromechanical Systems by means of Deep-Autoencoder by Arellano-Espitia, Francisco, Delgado-Prieto, Miguel, Martinez-Viol, Victor, Fernandez-Sobrino, Angel, Osornio-Rios, Roque Alfredo

“… In this regard, although several machine learning techniques have been classically considered, the recent appearance of deep-learning approaches represents an opportunity in the field to increase…”
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A Unified Unsupervised Gaussian Mixture Variational Autoencoder for High Dimensional Outlier Detection by Liao, Weixian, Guo, Yifan, Chen, Xuhui, Li, Pan

“… To tackle these challenges, in this paper, we propose a unified Unsupervised Gaussian Mixture Variational Autoencoder…”
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