Title: The big data mining forecasting model based on combination of improved manifold learning and deep learning
Authors: Xiurong Chen; Yixiang Tian
Addresses: School of Economics and Commerce, Zhengzhou University of Aeronautics, Zhengzhou 450046, China ' School of Management and Economics, University of Electronic Science and Technology of China, Chengdu 611731, China
Abstract: In this paper, we use the combination of Local Linear Embedding (LLE) with Continuous Deep Belief Networks (CDBN) as the input of RBF, and construct a mixed-feature RBF model. However, LLE depends too much on the local domain which is not easy to be determined, so we propose a new method, Kernel Entropy Linear Embedding (KELE) which uses Kernel Entropy Component Analysis (KECA) to transfer the non-linear problem into linear problem. CDBN has the difficulty in confirming network structure and lacks supervision, so we improve the situations by using the kernel entropy information obtained from KECA, which is called KECDBN. In the empirical part, we use the foreign exchange rate time series to examine the effects of the improved methods, and results show that both the KELE and the KECDBN show better effects in reducing dimensionality and extracting features, respectively, an also improve the prediction accuracy of the mixed-feature RBF.
Keywords: LLE; local linear embedding; CDBN; continuous deep belief network; KECA; kernel entropy component analysis; KELE; kernel entropy linear embedding; KECDBN; kernel entropy continuous deep belief network.
International Journal of Grid and Utility Computing, 2019 Vol.10 No.2, pp.119 - 131
Received: 11 Aug 2017
Accepted: 12 Sep 2017
Published online: 19 Jan 2019 *