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- W4311595000 abstract "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." @default.
- W4311595000 created "2022-12-27" @default.
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- W4311595000 date "2022-01-08" @default.
- W4311595000 modified "2023-09-28" @default.
- W4311595000 title "Multi-View Non-negative Matrix Factorization Discriminant Learning via Cross Entropy Loss" @default.
- W4311595000 doi "https://doi.org/10.48550/arxiv.2201.04726" @default.
- W4311595000 hasPublicationYear "2022" @default.
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