Multi-View Non-negative Matrix Factorization Discriminant Learning via Cross Entropy Loss
PROCEEDINGS OF THE 32ND 2020 CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2020)(2020)
摘要
Multi-view learning accomplishes the task objectives of classification by leveraging the relationships between different views of the same object. Most existing methods usually focus on consistency and complementarity between multiple views. But not all of this information is useful for classification tasks. Instead, it is the specific discriminating information that plays an important role. Zhong Zhang et al explores the discriminative and non-discriminative information existing in common and view-specific parts among different views via joint non-negative matrix factorization. In this paper, we improve this algorithm on this basis by using the cross entropy loss function to constrain the objective function better. At last, we implement better classification effect than original on the same data sets and show its superiority over many state-of-the-art algorithms.
更多查看译文
关键词
Multi-View learning,Matrix factorization,Cross entropy loss,Discriminative learning,Classification
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要