Binary Orthogonal Non-negative Matrix Factorization
This work addresses clustering and classification tasks, but appears incremental as it builds on existing non-negative matrix factorization techniques.
The authors tackled the problem of clustering and classification by proposing a binary orthogonal non-negative matrix factorization method, which achieved improved accuracy on real-world datasets compared to related techniques.
We propose a method for computing binary orthogonal non-negative matrix factorization (BONMF) for clustering and classification. The method is tested on several representative real-world data sets. The numerical results confirm that the method has improved accuracy compared to the related techniques. The proposed method is fast for training and classification and space efficient.