Results 261 to 270 of about 134,390 (294)

Multi-view intact space clustering

Pattern Recognition, 2017
Multi-view clustering is a hot research topic due to the urgent need for analyzing a vast amount of heterogeneous data. Although many multi-view clustering methods have been developed, they have not addressed the view-insufficiency issue. That is, most of the existing multi-view clustering methods assume that each individual view is sufficient for ...
Ling Huang 0002, Hong-Yang Chao, Chang-Dong Wang 0001   +2 more
openaire   +1 more source

Lifelong Multi-view Spectral Clustering

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023
In recent years, spectral clustering has become a well-known and effective algorithm in machine learning. However, traditional spectral clustering algorithms are designed for single-view data and fixed task setting. This can become a limitation when dealing with new tasks in a sequence, as it requires accessing previously learned tasks.
Hecheng Cai, Yuze Tan, Shudong Huang, Jiancheng Lv 0001   +3 more
openaire   +1 more source

Partial multi-view spectral clustering

Neurocomputing, 2018
Abstract The partial multi-view clustering is an emerging hot research area. For example, in web page clustering, the web page content or its linkage information may suffer from the missing of some data. Traditional multi-view clustering methods deal with this kind of problem by completing and clustering separately and thus degrade the clustering ...
Yang Cai, Yuanyuan Jiao, Wenzhang Zhuge, Hong Tao, Chenping Hou   +4 more
openaire   +1 more source

Incremental multi-view spectral clustering

Knowledge-Based Systems, 2019
Abstract Multi-view learning has attracted increasing attention in recent years, and the existing multi-view learning methods learn a consensus result by collecting all views. These methods have two obvious limitations. First, it is not scalable; with limited computational resources it would be difficult, if not impossible, to collect and process a ...
Peng Zhou 0006, Yi-Dong Shen, Liang Du 0003, Fan Ye, Xuejun Li 0001   +4 more
openaire   +1 more source

Multi-view Clustering on Relational Data

2014
Clustering is a popular task in knowledge discovery. In this chapter we illustrate this fact with a new clustering algorithm that is able to partition objects taking into account simultaneously their relational descriptions given by multiple dissimilarity matrices.
Francisco de A. T. de Carvalho, Yves Lechevallier, Thierry Despeyroux, Filipe M. de Melo   +3 more
openaire   +1 more source

Incomplete multi-view spectral clustering

Journal of Intelligent & Fuzzy Systems, 2020
 Multi-view clustering algorithms mostly apply to data without incomplete instances. However, in real-world applications, representations for the same instance are probably absent from several but not all views. This incompleteness disables traditional multi-view clustering methods from grouping incomplete multi-view data.
Qianli Zhao, Linlin Zong, Xianchao Zhang 0001, Xinyue Liu 0002, Hong Yu 0005   +4 more
openaire   +1 more source

Multi-view Proximity Learning for Clustering

2018
In recent years, multi-view clustering has become a hot research topic due to the increasing amount of multi-view data. Among existing multi-view clustering methods, proximity-based method is a typical class and achieves much success. Usually, these methods need proximity matrices as inputs, which can be constructed by some nearest-neighbors-based ...
Kun-Yu Lin, Ling Huang 0002, Chang-Dong Wang 0001, Hong-Yang Chao   +3 more
openaire   +1 more source

Continual Multi-view Clustering

Proceedings of the 30th ACM International Conference on Multimedia, 2022
Xinhang Wan, Jiyuan Liu 0003, Weixuan Liang, Xinwang Liu 0002, Yi Wen 0001, En Zhu   +5 more
openaire   +1 more source

Latent Multi-view Subspace Clustering

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017
In this paper, we propose a novel Latent Multi-view Subspace Clustering (LMSC) method, which clusters data points with latent representation and simultaneously explores underlying complementary information from multiple views. Unlike most existing single view subspace clustering methods that reconstruct data points using original features, our method ...
Changqing Zhang 0002, Qinghua Hu, Huazhu Fu, Pengfei Zhu 0001, Xiaochun Cao   +4 more
openaire   +1 more source

The Multi-view Information Bottleneck Clustering

2007
In this paper, we propose a new algorithm for information bottleneck method in multi-view setting where instances have multiple independent representations. By introducing the two important conditions, conditional independence and compatibility, into the information bottleneck clustering, the compatible constraint maximizing the agreement between ...
Yan Gao, Shiwen Gu, Jianhua Li, Zhining Liao   +3 more
openaire   +1 more source