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Supervised Class-pairwise NMF for Data Representation and Classification
The objective of this work is to design an evolutionary framework to learn the hyper-parameter of the parameterized NMF and estimate the factorizing matrices in a supervised way to be more suitable for classification problems.
Non-Negative Matrix Factorization with Scale Data Structure Preservation
The model described in this paper belongs to the family of non-negative matrix factorization methods designed for data representation and dimension reduction.
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