Multi-View Non-negative Matrix Factorization Discriminant Learning via Cross Entropy Loss
This is an incremental improvement for multi-view learning tasks, enhancing classification accuracy by refining the objective function with cross-entropy loss.
The paper tackles the problem of improving multi-view classification by better capturing discriminative information, achieving superior classification performance compared to the original method and state-of-the-art algorithms on the same datasets.
Multi-view learning accomplishes the task objectives of classification by leverag-ing 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. explore the discriminative and non-discriminative information exist-ing in common and view-specific parts among different views via joint non-negative matrix factorization. In this paper, we improve this algorithm on this ba-sis 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.