时间:2024-03-01![]()
本文(Augmented Sparse Representation for Incomplete Multiview Clustering)原载IEEE Transactions on Neural Networks and Learning Syetems,由四川大学王竹教授等科研人员创作,系四川大学智慧法治超前部署学科系列学术成果。后续会持续分享四川大学智慧法治超前部署学科系列学术成果,欢迎大家阅读。
Incomplete multiview data are collected from mul
tiple sources or characterized by multiple modalities, where
the features of some samples or some views may be missing.
Incomplete multiview clustering (IMVC) aims to partition the
data into different groups by taking full advantage of the
complementary information from multiple incomplete views.
Most existing methods based on matrix factorization or subspace
learning attempt to recover the missing views or perform impu
tation of the missing features to improve clustering performance.
However, this problem is intractable due to a lack of prior
knowledge, e.g., label information or data distribution, especially
when the missing views or features are completely damaged.
In this article, we proposed an augmented sparse representation
(ASR) method for IMVC. We first introduce a discriminative
sparse representation learning (DSRL) model, which learns the
sparse representations of multiple views as applied to measure
the similarity of the existing features. The DSRL model explores
complementary and consistent information by integrating the
sparse regularization item and a consensus regularization item,
respectively. Simultaneously, it learns a discriminative dictionary
from the original samples. The sparsity constrained optimiza
tion problem in the DSRL model can be efficiently solved
by the alternating direction method of multipliers (ADMM).
Then, we present a similarity fusion scheme, namely, a sparsity
augmented fusion of sparse representations, to obtain a sparsity
augmented similarity matrix across different views for spectral
clustering. Experimental results on several datasets demonstrate
the effectiveness of the proposed ASR method for IMVC.
Jie Chen, Shengxiang Yang, Xi Peng, Dezhong Peng, and ZhuWang. Augmented Sparse Representation for Incomplete Multiview Clustering. IEEE Transactions on Neural Networks and Learning Syetems, VOL. 35, NO. 3, MARCH 2024.(论文下载)