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We quantitatively evaluate the performance of STIC on both simulation and real sequencing datasets, the results of which indicate that STIC outperforms competing methods.Non-negative matrix factorization (NMF) is a dimensionality reduction technique based on high-dimensional mapping. It can effectively learn part-based representations. In this paper, we propose a method called Dual Hyper-graph Regularized Supervised Non-negative Matrix Factorization (HSNMF). To encode the geometric information of the data, the hyper-graph is introduced
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