Výsledky vyhledávání - Deep autoencoder-based clustering

Deep Convolutional Asymmetric Autoencoder-Based Spatial-Spectral Clustering Network for Hyperspectral Image Autor Liu, Baisen, Kong, Weili, Wang, Yan

“… In this paper, we propose a novel deep convolutional asymmetric autoencoder-based spatial-spectral clustering network (DCAAES2C-Net…”
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Matrix Factorization and Deep Autoencoder based Clustering Scheme for Large-scale UAV Networks Autor Fang, Jiaolan, Wang, Chan, Li, Rongpeng, Wei, Hanyu, Zhao, Minjian

“… Typically, clustering is widely adopted to reduce the degradation of network performance in large-scale UAV networks…”
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An autoencoder-based deep learning approach for clustering time series data Autor Tavakoli, Neda, Siami-Namini, Sima, Adl Khanghah, Mahdi, Mirza Soltani, Fahimeh, Siami Namin, Akbar

“… Second, an autoencoder-based deep learning model is built to model both known and hidden non-linear features of time series data…”
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DAC: Deep Autoencoder-based Clustering, a General Deep Learning Framework of Representation Learning Autor Lu, Si, Li, Ruisi

“… When those assumptions does not hold, these algorithms then might not work. In this paper, we propose DAC, Deep Autoencoder-based Clustering, a generalized data-driven framework to learn clustering representations using deep neuron networks…”
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Operationalization of the construct “Business model of a Bank”: clustering analyses with deep neural networks Autor Herdt, Manfred, Schulte-Mattler, Hermann

“…This paper presents a framework to operationalize the multidimensional construct of a bank's business model (BBM). We conceptualize the construct from a…”
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A Robust PCA Feature Selection To Assist Deep Clustering Autoencoder-Based Network Anomaly Detection Autor Nguyen, Van Quan, Nguyen, Viet Hung, Cao, Van Loi, Khac, Nhien - An Le, Shone, Nathan

“…This paper presents a novel method to enhance the performance of Clustering-based Autoencoder models for network anomaly detection…”
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Antenna Scanning Type Classification with Autoencoder Based Deep Clustering Autor Ozmen, Emirhan, Ozkazanc, Yakup

“…In this study, a deep learning-based algorithm is proposed for automatic detection of antenna scanning types used in EW systems…”
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A Novel Autoencoder based Federated Deep Transfer Learning and Weighted k-Subspace Network clustering for Intelligent Intrusion Detection for the Internet of Things Autor Lavanya, V. S., Anushiya, R.

“… In this research, suggest an Autoencoder based Deep Federated Transfer Learning (ADFTL) to conquer these obstacles…”
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Brain tumor classification using a hybrid deep autoencoder with Bayesian fuzzy clustering-based segmentation approach Autor Siva Raja, P.M., rani, Antony Viswasa

“… This paper developed a brain tumor classification using a hybrid deep autoencoder with a Bayesian fuzzy clustering-based segmentation approach…”
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Leveraging tensor kernels to reduce objective function mismatch in deep clustering Autor Trosten, Daniel J., Løkse, Sigurd, Jenssen, Robert, Kampffmeyer, Michael

“… In this work we study OFM in deep clustering, and find that the popular autoencoder-based approach to deep clustering can lead to both reduced clustering performance, and a significant amount of OFM…”
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Clustering Time Series Data through Autoencoder-based Deep Learning Models Autor Tavakoli, Neda, Siami-Namini, Sima, Mahdi Adl Khanghah, Fahimeh Mirza Soltani, Namin, Akbar Siami

“…). In particular, deep learning techniques are capable of capturing and learning hidden features in a given data sets and thus building a more accurate prediction model for clustering and labeling problem…”
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scTPC: a novel semisupervised deep clustering model for scRNA-seq data Autor Qiu, Yushan, Yang, Lingfei, Jiang, Hao, Zou, Quan

“… Results This study investigates a semisupervised clustering model called scTPC, which integrates the triplet constraint, pairwise constraint, and cross-entropy constraint based on deep learning…”
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Unsupervised Seismic Facies Deep Clustering Via Lognormal Mixture-Based Variational Autoencoder Autor Hua, Haowei, Qian, Feng, Zhang, Gulan, Yue, Yuehua

“… The dominant isolated learning-based SFA schemes have gained considerable attention and primarily focus on learning the best representation of prestack data and generating facies maps by clustering…”
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Autoencoder-based unsupervised clustering and hashing Autor Zhang, Bolin, Qian, Jiangbo

“…Faced with a large amount of data and high-dimensional data information in a database, the existing exact nearest neighbor retrieval methods cannot obtain…”
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A Convolutional Autoencoder-based Explainable Clustering Approach for Resting-State EEG Analysis Autor Ellis, Charles A., Miller, Robyn L., Calhoun, Vince D.

“… learning-based approaches with automated feature learning to cluster EEG. Those studies involve separately training an autoencoder and then performing clustering…”
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A New Graph Autoencoder-Based Multi-Level Kernel Subspace Fusion Framework for Single-Cell Type Identification Autor Wang, Juan, Qiao, Tian-Jing, Zheng, Chun-Hou, Liu, Jin-Xing, Shang, Jun-Liang

“… Although many single-cell clustering methods have been developed recently, few can fully exploit the deep potential relationships between cells, resulting in suboptimal clustering…”
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Achieving deep clustering through the use of variational autoencoders and similarity-based loss Autor Ma, He

“… In this work, a novel variational autoencoder-based deep clustering algorithm is proposed. It treats the Gaussian mixture model as the prior latent space and uses…”
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Interpretable unsupervised learning enables accurate clustering with high-throughput imaging flow cytometry Autor Zhang, Zunming, Chen, Xinyu, Tang, Rui, Zhu, Yuxuan, Guo, Han, Qu, Yunjia, Xie, Pengtao, Lian, Ian Y., Wang, Yingxiao, Lo, Yu-Hwa

“… We present an unsupervised deep embedding algorithm, the Deep Convolutional Autoencoder-based Clustering (DCAEC…”
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Patient Clustering for Vital Organ Failure Using ICD Code With Graph Attention Autor Liu, Zhangdaihong, Hu, Ying, Wu, Xuan, Mertes, Gert, Yang, Yang, Clifton, David A.

“… We employ an autoencoder-based deep clustering architecture jointly trained with a K-means loss, and a non-linear dimension reduction is performed to obtain patient clusters on the MIMIC-III dataset. Results…”
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scCompressSA: dual-channel self-attention based deep autoencoder model for single-cell clustering by compressing gene–gene interactions Autor Zhang, Wei, Yu, Ruochen, Xu, Zeqi, Li, Junnan, Gao, Wenhao, Jiang, Mingfeng, Dai, Qi

“…Background Single-cell clustering has played an important role in exploring the molecular mechanisms about cell differentiation and human diseases…”
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