Multi-View Non-negative Matrix Factorization Discriminant Learning via Cross Entropy Loss

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.

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