ORL Data derived from public domain resources. The data that support the findings of this study are available at HYPERLINK "#bib32". The data that support the findings of this study are available at HYPERLINK "#bib33".ĮTH80-1 Database Data derived from public domain resources. The experiments in our manuscript titled Tensor Local Linear Embedding with Global Subspace Projection Optimization contains five data sets:įaces 94 male Database Data derived from public domain resources. The extensive experimental results on tensor data classification and clustering have demonstrated the proposed algorithms performed well. In particular, two novel TDR algorithms are developed by the ensemble of tensor local feature preservation and global subspace projection distance minimisation, which express the subspace projection optimisation as an iteration optimisation problem and a Rayleigh quotient problem, respectively. The aim of this strategy is to find an optimal low-dimensional subspace for TDR. Furthermore, a global subspace projection distance minimisation strategy is introduced to extract the global characteristic of the tensor data. Firstly, we analyse the local linear feature of tensor data for learning the linear separable embedding of the tenor data. In this paper, a novel tensor dimensionality reduction (TDR) approach is proposed, which maintains the local geometric structure of tensor data by tensor local linear embedding and explores the global feature by optimising global subspace projection. IET Generation, Transmission & Distribution.IET Electrical Systems in Transportation.IET Cyber-Physical Systems: Theory & Applications.IET Collaborative Intelligent Manufacturing.CAAI Transactions on Intelligence Technology.
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