Feature Extraction of High-dimensional Data Based on J-HOSVD for Cyber-Physical-Social Systems

With the further integration of Cyber-Physical-Social systems (CPSSs), there is explosive growth of the data in CPSSs. How to discover effective information or knowledge from CPSSs big data and provide support for subsequent learning tasks has become a core issue. Moreover, modern applications in CPSSs increasingly rely on the processing and analysis of high-dimensional data; the correlation and internal structure of these high-dimensional data are gradually becoming more complex, which further makes traditional machine learning algorithms a little inadequate in processing these data. In this article, we propose two general dimension reduction and feature extraction methods for high-dimensional data based on joint tensor decomposition, namely core feature extraction methods and factor feature extraction methods, which can effectively mine out the common components and hidden patterns of high-dimensional data by joint analysis while maintaining the original data structure. We also verified the effectiveness of the methods from both theoretical and practical aspects. Furthermore, we extend the two feature extraction methods to the tensor distance scenario and illustrate that the compressed features extracted by our models can keep the global information of original data well. Finally, we evaluated proposed methods on two benchmark datasets through classification tasks, and experimental results show that the low-dimensional features extracted by the proposed models have higher classification accuracy than the direct classification of the original data, which further verifies the effectiveness and robustness of our methods.