Non-negative Tucker decomposition(NTD) has been developed as a crucial method for non-negative tensor data representation.However, NTD is essentially an unsupervised method and cannot take advantage of label informati...Non-negative Tucker decomposition(NTD) has been developed as a crucial method for non-negative tensor data representation.However, NTD is essentially an unsupervised method and cannot take advantage of label information. In this paper, we claim that the low-dimensional representation extracted by NTD can be treated as the predicted soft-clustering coefficient matrix and can therefore be learned jointly with label propagation in a unified framework. The proposed method can extract the physicallymeaningful and parts-based representation of tensor data in their natural form while fully exploring the potential ability of the given labels with a nearest neighbors graph. In addition, an efficient accelerated proximal gradient(APG) algorithm is developed to solve the optimization problem. Finally, the experimental results on five benchmark image data sets for semi-supervised clustering and classification tasks demonstrate the superiority of this method over state-of-the-art methods.展开更多
基金This work was supported by the National Natural Science Foundation of China(Grant Nos.62073087,U191140003,6197309 and 61973090)the Key-Area Research and Development Program of Guangdong Province(Grant Nos.2019B010154002 and 2019B010118001)。
文摘Non-negative Tucker decomposition(NTD) has been developed as a crucial method for non-negative tensor data representation.However, NTD is essentially an unsupervised method and cannot take advantage of label information. In this paper, we claim that the low-dimensional representation extracted by NTD can be treated as the predicted soft-clustering coefficient matrix and can therefore be learned jointly with label propagation in a unified framework. The proposed method can extract the physicallymeaningful and parts-based representation of tensor data in their natural form while fully exploring the potential ability of the given labels with a nearest neighbors graph. In addition, an efficient accelerated proximal gradient(APG) algorithm is developed to solve the optimization problem. Finally, the experimental results on five benchmark image data sets for semi-supervised clustering and classification tasks demonstrate the superiority of this method over state-of-the-art methods.
