Results 241 to 250 of about 463,538 (280)

Multi-view Subspace Clustering

2015 IEEE International Conference on Computer Vision (ICCV), 2015
For many computer vision applications, the data sets distribute on certain low-dimensional subspaces. Subspace clustering is to find such underlying subspaces and cluster the data points correctly. In this paper, we propose a novel multi-view subspace clustering method. The proposed method performs clustering on the subspace representation of each view
Hongchang Gao, Feiping Nie, Xuelong Li, Heng Huang   +3 more
openaire   +1 more source

Tensorized Incomplete Multi-View Kernel Subspace Clustering

SSRN Electronic Journal, 2023
Recently considerable advances have been achieved in the incomplete multi-view clustering (IMC) research. However, the current IMC works are often faced with three challenging issues. First, they mostly lack the ability to recover the nonlinear subspace structures in the multiple kernel spaces.
Guang-Yu Zhang, Dong Huang, Chang-Dong Wang   +2 more
openaire   +2 more sources

Partial Multi-View Clustering

Proceedings of the AAAI Conference on Artificial Intelligence, 2014
Real data are often with multiple modalities or comingfrom multiple channels, while multi-view clusteringprovides a natural formulation for generating clustersfrom such data. Previous studies assumed that each exampleappears in all views, or at least there is one viewcontaining all examples.
Shao-Yuan Li, Yuan Jiang, Zhi-Hua Zhou
openaire   +1 more source

Multi-view clustering ensembles

2013 International Conference on Machine Learning and Cybernetics, 2013
Multi-view clustering and clustering ensembles have become increasingly popular in recent years. Multi-view clustering employs the relationship between views to cluster data; clustering ensembles combine different component clusterings to a better final partition.
null Xijiong Xie, null Shiliang Sun
openaire   +1 more source

Collaborative multi-view clustering

The 2013 International Joint Conference on Neural Networks (IJCNN), 2013
The purpose of this article is to introduce a new collaborative multi-view clustering approach based on a probabilistic model. The aim of collaborative clustering is to reveal the common underlying structure of data spread across multiple data sites by applying clustering techniques.
Mohamad Ghassany, Nistor Grozavu, Younes Bennani   +2 more
openaire   +1 more source

Projective Incomplete Multi-View Clustering

IEEE Transactions on Neural Networks and Learning Systems
Due to the rapid development of multimedia technology and sensor technology, multi-view clustering (MVC) has become a research hotspot in machine learning, data mining, and other fields and has been developed significantly in the past decades. Compared with single-view clustering, MVC improves clustering performance by exploiting complementary and ...
Shijie Deng, Jie Wen, Chengliang Liu, Ke Yan, Gehui Xu, Yong Xu   +5 more
openaire   +2 more sources

Split Multiplicative Multi-View Subspace Clustering

IEEE Transactions on Image Processing, 2019
Various subspace clustering methods have been successively developed to process multi-view datasets. Most of the existing methods try to obtain a consensus structure coefficient matrix based on view-specific subspace recoveries. However, since view-specific structures contain individualized components that are intrinsically different from the consensus
Zhiyong Yang, Qianqian Xu, Weigang Zhang, Xiaochun Cao, Qingming Huang   +4 more
openaire   +2 more sources

Constrained Multi-View Video Face Clustering

IEEE Transactions on Image Processing, 2015
In this paper, we focus on face clustering in videos. To promote the performance of video clustering by multiple intrinsic cues, i.e., pairwise constraints and multiple views, we propose a constrained multi-view video face clustering method under a unified graph-based model.
Cao, Xiaochun, Zhang, Changqing, Zhou, Chengju, Fu, Huazhu, Foroosh, Hassan   +4 more
openaire   +3 more sources

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.
Despeyroux, Thierry, de Carvahlo, Francisco de A.T., Lechevallier, Yves, de Melo, Filipe   +3 more
openaire   +1 more source

Consensus Guided Multi-View Clustering

ACM Transactions on Knowledge Discovery from Data, 2018
In recent decades, tremendous emerging techniques thrive the artificial intelligence field due to the increasing collected data captured from multiple sensors. These multi-view data provide more rich information than traditional single-view data.
Hongfu Liu, Yun Fu
openaire   +1 more source