Algorithm 1: Improved Sparse Non-Negative Matrix Factorization

Step 1. Initialize non-negative matrices W and H randomly

Step 2. Extract the constraint reference vector $\overrightarrow{r}$ with the feature of the source signal

Step 3. Calculate the initial value of the objective function from Equation (15)

Step 4. According to Equations (11) and (12), update the matrices W and H alternately and iteratively

Step 5. If the objective function converges, the iteration is stopped, and the matrices W and H are outputted; otherwise, steps (3) and (4) are performed cyclically