Search Results - Deep autoencoder-based clustering*

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

“… In this paper, we propose a novel deep convolutional asymmetric autoencoder-based spatial-spectral clustering network (DCAAES2C-Net…”
Get full text

Matrix Factorization and Deep Autoencoder based Clustering Scheme for Large-scale UAV Networks by 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…”
Get full text

An autoencoder-based deep learning approach for clustering time series data by 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…”
Get full text

DAC: Deep Autoencoder-based Clustering, a General Deep Learning Framework of Representation Learning by 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…”
Get full text

Operationalization of the construct “Business model of a Bank”: clustering analyses with deep neural networks by 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…”
Get full text

A Robust PCA Feature Selection To Assist Deep Clustering Autoencoder-Based Network Anomaly Detection by 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…”
Get full text

Antenna Scanning Type Classification with Autoencoder Based Deep Clustering by 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…”
Get full text

Brain tumor classification using a hybrid deep autoencoder with Bayesian fuzzy clustering-based segmentation approach by 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…”
Get full text

A Novel Autoencoder based Federated Deep Transfer Learning and Weighted k-Subspace Network clustering for Intelligent Intrusion Detection for the Internet of Things by Lavanya, V. S., Anushiya, R.

“… In this research, suggest an Autoencoder based Deep Federated Transfer Learning (ADFTL) to conquer these obstacles…”
Get full text

Unsupervised Seismic Facies Deep Clustering Via Lognormal Mixture-Based Variational Autoencoder by 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…”
Get full text

Leveraging tensor kernels to reduce objective function mismatch in deep clustering by 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…”
Get full text

scTPC: a novel semisupervised deep clustering model for scRNA-seq data by 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…”
Get full text

scCompressSA: dual-channel self-attention based deep autoencoder model for single-cell clustering by compressing gene–gene interactions by 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…”
Get full text

Self‐supervised representation learning of metro interior noise based on variational autoencoder and deep embedding clustering by Wang, Yang, Xiao, Hong, Zhang, Zhihai, Guo, Xiaoxuan, Liu, Qiang

“… Recently, methods for identifying defects based on interior noise signals are emerging, among which representation learning is the foundation for deep neural network models to understand the key…”
Get full text

Multi-View Deep Clustering based on AutoEncoder by Dong, Shihao, Xu, Huiying, Zhu, Xinzhong, Guo, XiFeng, Liu, Xinwang, Wang, Xia

“…In recent years, with the development of deep learning, replacing traditional clustering methods with subspaces extracted by deep neural networks will help better clustering performance…”
Get full text

Interpretable unsupervised learning enables accurate clustering with high-throughput imaging flow cytometry by 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…”
Get full text

Achieving deep clustering through the use of variational autoencoders and similarity-based loss by 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…”
Get full text

Patient Clustering for Vital Organ Failure Using ICD Code With Graph Attention by 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…”
Get full text

Clustering Time Series Data through Autoencoder-based Deep Learning Models by 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…”
Get full text

Deep embedding clustering based on contractive autoencoder by Diallo, Bassoma, Hu, Jie, Li, Tianrui, Khan, Ghufran Ahmad, Liang, Xinyan, Zhao, Yimiao

“… To that end, we first introduce Contractive Autoencoders. Then we propose a deep embedding clustering framework based on contractive autoencoder (DECCA…”
Get full text