.png){kind=icon}
返回
Label prediction based constrained non-negative matrix factorization for semi-supervised multi-view classification
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2022.09.087.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Semi-supervised multi-view classification can improve the performance by leveraging the information from both labeled and unlabeled data. But it is often a challenge to capture the information from the unla-beled multi-view data. By analyzing the relation between labeled and unlabeled data under multi-view scenario, we propose a novel model with the ability of leveraging the latent label information from the unlabeled data. In our model, a label prediction (LP) term is proposed to jointly obtain the predicted labels of unlabeled data from multiple views. The LP term is integrated into a constrained non -negative matrix factorization based multi-view framework. In this way, the LP and the multi-view rep-resentation learning are integrated into one joint learning problem, where they boost each other. Particularly, the predicted label vector is formulated to be the one-hot vector, such that the labels can be obtained directly. Moreover, we propose a new lemma about the gradient of the '2;1 norm in the case of 3-factor matrix decomposition and its corollary about multi-factor matrix decomposition. Based on which, we develop an efficient algorithm and prove its convergence. Experimental results verify that our method can obtain state-of-the-art performance.(c) 2022 Elsevier B.V. All rights reserved.
Keyword:
Label prediction
Semi-supervised learning
Multi-view classification
Constrained non-negative matrix
factorization
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
分享收藏Semi-supervised person re-identification using multi-view clustering基于多视角聚类的半监督人物再识别.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏
Graph regularized nonnegative matrix factorization with label discrimination for data clustering带标签判别的图正则化非负矩阵分解用于数据聚类NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏
查看更多内容