A Deep Matrix Factorization Method for Learning Attribute Representations
A Deep Matrix Factorization Method for Learning Attribute Representations
Semi-Non-negative Matrix Factorization is a technique that learns a low-dimensional representation of a dataset that lends itself to a clustering interpretation. It is possible that the mapping between this new representation and our original data matrix contains rather complex hierarchical information with implicit lower-level hidden attributes, that classical one level …