Suchergebnisse - Deep autoencoder-based clustering

Deep Convolutional Asymmetric Autoencoder-Based Spatial-Spectral Clustering Network for Hyperspectral Image von 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 von 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 von 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 von 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 von 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 von 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 von 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 von 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 von 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 von 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 von 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 von 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 von 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 von 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 von 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 von 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 von 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 von 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 von 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 von 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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