YANG Zhen, YAO Fei, FAN Kefeng, HUANG Jian. Text Dimensionality Reduction with Mutual Information Preserving Mapping[J]. Chinese Journal of Electronics, 2017, 26(5): 919-925. doi: 10.1049/cje.2017.08.020
Citation: YANG Zhen, YAO Fei, FAN Kefeng, HUANG Jian. Text Dimensionality Reduction with Mutual Information Preserving Mapping[J]. Chinese Journal of Electronics, 2017, 26(5): 919-925. doi: 10.1049/cje.2017.08.020

Text Dimensionality Reduction with Mutual Information Preserving Mapping

doi: 10.1049/cje.2017.08.020
Funds:  This work is supported by the National Natural Science Foundation of China (No.61671030), the Excellent Talents Foundation of Beijing, the Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions (No.CIT&TCD201404052), and the Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex Systems (No.15205).
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  • Corresponding author: FAN Kefeng (corresponding author) was born in 1978. He received the Ph.D. degree in test signal processing from Xidian University, Shaanxi, China, in 2007. From 2008 to 2010, he was a postdoctoral stuff in the State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications. Now, He is deputy director of the research center of CESI. His current research interests include signal processing and cyberspace security. (Email:fankf@cesi.ac.cn)
  • Received Date: 2015-07-24
  • Rev Recd Date: 2016-04-08
  • Publish Date: 2017-09-10
  • With the explosion of information, it is becoming increasingly difficult to get what is really wanted. Dimensionality reduction is the first step in efficient processing of large data. Although dimensionality can be reduced in many ways, little work has been done to achieve dimensionality reduction without changing the inner semantic relationship among high dimension data. To remedy this problem, we introduced a manifold learning based method, named Mutual information preserving mapping (MIPM), to explore the low-dimensional, neighborhood and mutual information preserving embeddings of highdimensional inputs. Experimental results show that the proposed method is effective for the text dimensionality reduction task. The MIPM was used to develop a temporal summarization system for efficiently monitoring the information associated with an event over time. With respect to the established baselines, results of these experiments show that our method is effective in the temporal summarization.
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