Suchergebnisse - "Multi-view data/clustering"

ORKM: An R package for online multi-view data clustering von Yu, Miao, Li, Shu, Guo, Guangbao

“… We propose a package called ORKM, which implements the ORKMC (Online Regularized K-Means Clustering) method for handling online multi-view or single-view data, …”
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Multi-view data clustering via non-negative matrix factorization with manifold regularization von Khan, Ghufran Ahmad, Hu, Jie, Li, Tianrui, Diallo, Bassoma, Wang, Hongjun

“… Nowadays, non-negative matrix factorization (NMF) based cluster analysis for multi-view data shows impressive behavior in machine learning. Usually, multi-view …”
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Robust multi-view data clustering with multi-view capped-norm K-means von Huang, Shudong, Ren, Yazhou, Xu, Zenglin

“… Real-world data sets are often comprised of multiple representations or views which provide different and complementary aspects of information. Multi-view …”
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Feature decomposition and structural learning for multi-diverse and multi-view data clustering von Zhang, Yong, Liu, Da, Jiang, Li, Wang, Huibing, Liu, Wenzhe

“… and structural learning for multi-diverse and multi-view data clustering ( FDSL _ M 2 C ). FDSL _ M 2 C utilizes a flexible feature decomposition approach to extract latent and consensus representations from distinct views …”
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A Novel Approach to Learning Consensus and Complementary Information for Multi-View Data Clustering von Luong, Khanh, Nayak, Richi

“… Effective methods are required to be developed that can deal with the multi-faceted nature of the multi-view data. We design a factorization-based loss …”
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Unified Multi-view Data Clustering: Simultaneous Learning of Consensus Coefficient Matrix and Similarity Graph von Dornaika, F., El Hajjar, S., Charafeddine, J., Barrena, N.

“… Integrating data from multiple sources or views has become increasingly common in data analysis, particularly in fields like healthcare, finance, and social …”
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An End-to-End Approach for Graph-Based Multi-View Data Clustering von Dornaika, Fadi, El Hajjar, Sally

“… Clustering data from different sources or views is a key challenge in real-world applications. While traditional graph-based methods are effective at capturing …”
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Multi-layer manifold learning for deep non-negative matrix factorization-based multi-view clustering von Luong, Khanh, Nayak, Richi, Balasubramaniam, Thirunavukarasu, Bashar, Md Abul

“… •An orthogonal deep non-negative matrix factorization (Deep-NMF) framework that aims to learn the non-linear parts-based representation for multi-view data is …”
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Towards unsupervised radiograph clustering for COVID-19: The use of graph-based multi-view clustering von Dornaika, F., Hajjar, S. El, Charafeddine, J.

“… Automatic classification methods widely used for diagnosing and analyzing COVID-19 cases. These methods assume known labels and rely on a single view of the …”
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Learning Inter- and Intra-Manifolds for Matrix Factorization-Based Multi-Aspect Data Clustering von Luong, Khanh, Nayak, Richi

“… Clustering on the data with multiple aspects, such as multi-view or multi-type relational data, has become popular in recent years due to their wide …”
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Weighted Multi-View Data Clustering via Joint Non-Negative Matrix Factorization von Khan, Ghufran Ahmad, Hu, Jie, Li, Tianrui, Diallo, Bassoma, Huang, Qianqian

“… In recent years, datasets which exist in present world are comprising of various representations of the data or in multiview environment, which frequently give …”
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Semi-Supervised Learning and Feature Fusion for Multi-view Data Clustering von Salman, Hadi, Zhan, Justin

“… Generative Adversarial Networks GANs have become widely used in Single-view classification tasks. Nowadays, most of the data have multiple views and each view …”
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Correlated and Uncorrelated Feature Co-Learning with Dual Graph Regularization for Multi-View Data Clustering von Sun, Tingting, Mo, Chunyang, Zhao, Liang, Chen, Zhikui

“… Therefore, the performance of them may be seriously degraded. To tackle it, in this paper, we develop a new subspace fusion model for multi-view data clustering …”
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Hessian Regularization Based Factorization Algorithm Combining Multi-view and Non-negative Matrix von WANG Chaofeng,SHI Jun,WU Jinjie,ZHU Jie

“… Non-negative matrix does not consider the manifold of data when represents multi-view data,which results in the ineffective express of the data internal …”
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GCFAgg: Global and Cross-View Feature Aggregation for Multi-View Clustering von Yan, Weiqing, Zhang, Yuanyang, Lv, Chenlei, Tang, Chang, Yue, Guanghui, Liao, Liang, Lin, Weisi

“… Multi-view clustering can partition data samples into their categories by learning a consensus representation in unsupervised way and has received more and …”
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Learning the consensus and complementary information for large-scale multi-view clustering von Liu, Maoshan, Palade, Vasile, Zheng, Zhonglong

“… The multi-view data clustering has attracted much interest from researchers, and the large-scale multi-view clustering has many important applications and significant research value …”
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Dual regularized multi-view non-negative matrix factorization for clustering von Luo, Peng, Peng, Jinye, Guan, Ziyu, Fan, Jianping

“… other. Synthesizing multi-view features for data representation can lead to more comprehensive data description, which may further allow us to find more effective solutions for multi-view data clustering …”
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Projective Incomplete Multi-View Clustering von Deng, Shijie, Wen, Jie, Liu, Chengliang, Yan, Ke, Xu, Gehui, Xu, Yong

“… Due to the rapid development of multimedia technology and sensor technology, multi-view clustering (MVC) has become a research hotspot in machine learning, …”
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Weighted Multi-view Clustering with Feature Selection von Xu, Yu-Meng, Wang, Chang-Dong, Lai, Jian-Huang

“… In recent years, combining multiple sources or views of datasets for data clustering has been a popular practice for improving clustering accuracy. As …”
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Auto-weighted multi-view co-clustering via fast matrix factorization von Nie, Feiping, Shi, Shaojun, Li, Xuelong

“… •Distinguishing the existing multi-view clustering methods, the proposed approaches involve constraints of indicator matrix in matrix decomposition. Due to the …”
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