Results 231 to 240 of about 463,538 (280)

Generalized Latent Multi-View Subspace Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020
Subspace clustering is an effective method that has been successfully applied to many applications. Here, we propose a novel subspace clustering model for multi-view data using a latent representation termed Latent Multi-View Subspace Clustering (LMSC). Unlike most existing single-view subspace clustering methods, which directly reconstruct data points
Changqing Zhang, Huazhu Fu, Qinghua Hu, Xiaochun Cao, Yuan Xie, Dacheng Tao, Dong Xu   +6 more
openaire   +4 more sources

Sequential multi-view subspace clustering

Neural Networks, 2022
Self-representation based subspace learning has shown its effectiveness in many applications, but most existing methods do not consider the difference between different views. As a result, the learned self-representation matrix cannot well characterize the clustering structure.
Lei, Fangyuan, Li, Qin
openaire   +2 more sources

Binary Multi-View Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019
Clustering is a long-standing important research problem, however, remains challenging when handling large-scale image data from diverse sources. In this paper, we present a novel Binary Multi-View Clustering (BMVC) framework, which can dexterously manipulate multi-view image data and easily scale to large data.
Zheng Zhang, Li Liu, Fumin Shen, Heng Tao Shen, Ling Shao   +4 more
openaire   +3 more sources

Multi-View Clustering

Fourth IEEE International Conference on Data Mining (ICDM'04), 2005
We consider clustering problems in which the available attributes can be split into two independent subsets, such that either subset suffices for learning. Example applications of this multi-view setting include clustering of Web pages which have an intrinsic view (the pages themselves) and an extrinsic view (e.g., anchor texts of inbound hyperlinks ...
S. Bickel, T. Scheffer
openaire   +1 more source

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, Hong-Yang Chao, Chang-Dong Wang   +2 more
openaire   +1 more source

Dual self-paced multi-view clustering

Neural Networks, 2021
By utilizing the complementary information from multiple views, multi-view clustering (MVC) algorithms typically achieve much better clustering performance than conventional single-view methods. Although in this field, great progresses have been made in past few years, most existing multi-view clustering methods still suffer the following shortcomings:
Zongmo Huang, Yazhou Ren, Xiaorong Pu, Lili Pan, Dezhong Yao, Guoxian Yu   +5 more
openaire   +2 more sources

Reliable Multi-View Clustering

Proceedings of the AAAI Conference on Artificial Intelligence, 2018
With the advent of multi-view data, multi-view learning (MVL) has become an important research direction in machine learning. It is usually expected that multi-view algorithms can obtain better performance than that of merely using a single view.
Hong Tao, Chenping Hou, Xinwang Liu, Tongliang Liu, Dongyun Yi, Jubo Zhu   +5 more
openaire   +1 more source