Paper Info
Title
Partial Multi-view Subspace Clustering.
Abstract
For many real-world multimedia applications, data are often described by multiple views. Therefore, multi-view learning researches are of great significance. Traditional multi-view clustering methods assume that each view has complete data. However, missing data or partial data are more common in real tasks, which results in partial multi-view learning. Therefore, we propose a novel multi-view clustering method, called Partial Multi-view Subspace Clustering (PMSC), to address the partial multi-view problem. Unlike most existing partial multi-view clustering methods that only learn a new representation of the original data, our method seeks the latent space and performs data reconstruction simultaneously to learn the subspace representation. The learned subspace representation can reveal the underlying subspace structure embedded in original data, leading to a more comprehensive data description. In addition, we enforce the subspace representation to be non-negative, yielding an intuitive weight interpretation among different data. The proposed method can be optimized by the Augmented Lagrange Multiplier (ALM) algorithm. Experiments on one synthetic dataset and four benchmark datasets validate the effectiveness of PMSC under the partial multi-view scenario.
Year
DOI
Venue
2018
10.1145/3240508.3240679
MM '18: ACM Multimedia Conference Seoul Republic of Korea October, 2018
Keywords
Field
DocType
Partial multi-view data, subspace clustering, latent space, subspace structure
Computer vision,Subspace clustering,Subspace topology,Data reconstruction,Pattern recognition,Computer science,Artificial intelligence,Missing data,Cluster analysis,Augmented lagrange multiplier,Data description
Conference
ISBN
Citations 
PageRank 
978-1-4503-5665-7
5
0.39
References 
Authors
25
5
Name
Order
Citations
PageRank
Nan Xu150.73
Yanqing Guo2356.24
Xin Zheng3144.92
Wang Qianyu4122.22
Xiangyang Luo553966.85