Constrained Nonnegative Tensor Factorization for Clustering

Constrained clustering through matrix factorization has been shown to largely improve clustering accuracy by incorporating prior knowledge into the factorization process. Although it has been well studied, none of them deal with constrained multi-way data factorization. Multi-way data or Tensors are encoded as high-order data structures. They can be seen as the generalization of matrices. One typical tensor is multiple two-way data/matrices in different time periods. To the best of our knowledge, this paper is the first work developing two general formulation of constrained nonnegative tensor factorization. An extensive experiment conducts a comparative study on the proposed constrained nonnegative tensor factorization and other state-of-the-art algorithms.