Semi-supervised non-negative matrix factorization with structure preserving for image clustering
Description
The code for paper '' Semi-supervised non-negative matrix factorization with structure preserving for image clustering''. This paper constructs a new label matrix with weights and further construct a label constraint regularizer to both utilize the label information and maintain the intrinsic structure of NMF. Based on the label constraint regularizer, the basis images of labeled data are extracted for monitoring and modifying the basis images learning of all data by establishing a basis regularizer. By incorporating the label constraint regularizer and the basis regularizer into NMF, a new semi-supervised NMF method is introduced. The proposed method is applied to image clustering and experimental results demonstrate the effectiveness of the proposed method in contrast with state-of-the-art unsupervised and semi-supervised algorithms.