In through-wall radar imaging (TWRI), the presence of the wall greatly reduces the performance of target detection algorithms. The signal reflected from the wall is stronger than the signal reflected from the target and masks the target. The physical properties of the wall or the reflections from the back and side walls in the environment where the target is located make the problem even more difficult. Within the scope of this study, the non-negative matrix factorization (NMF)-based approaches that we proposed for clutter removal in ground penetrating radar systems were adapted to the TWR problem. Moreover, a new NMF-based method which provides a better modelling of the wall component using sparsity constraint is introduced. Comparison with traditional subspace-based methods such as principal component analysis, singular value decomposition and low rank and sparse method robust principal component analysis for an experimental dataset validates that sparsity-guided NMF-based methods provide the best results.