Random Multi-Graphs: A semi-supervised learning framework for classification of high dimensional data |
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| Affiliation: | 1. Department of Computer Science and Technology, Ocean University of China, No. 238 Songling Road, Qingdao 266100, China;2. Department of Science and Information, Agriculture University of Qingdao, No. 700 Changcheng Road, Qingdao 266109, China;1. College of Mathematics and Computer Science, Fuzhou University, Fuzhou350116, China;2. Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou 350116, China;3. Tan Kah Kee College, Xiamen University, Zhangzhou363105, China;1. School of Automation, Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing 100081, China;2. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China;3. Key Laboratory of Autonomous Navigation and Control for Deep Space Exploration, Ministry of Industry and Information Technology, China;1. University of Sassari, Italy;2. Colorado State University, USA;3. University of Notre Dame, USA;4. University of Salerno, Italy;1. Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190, China;2. University of the Chinese Academy of Sciences, Beijing 100049, China;3. National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing 100029, China;4. Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, China;5. Electronic Engineering and Computer Science, Peking University, Beijing 100871, China;1. School of Engineering, Brown University, Providence, RI, USA;2. Perceiving Systems, Max Planck Institute for Intelligent Systems, Tubingen, Germany |
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| Abstract: | Currently, high dimensional data processing confronts two main difficulties: inefficient similarity measure and high computational complexity in both time and memory space. Common methods to deal with these two difficulties are based on dimensionality reduction and feature selection. In this paper, we present a different way to solve high dimensional data problems by combining the ideas of Random Forests and Anchor Graph semi-supervised learning. We randomly select a subset of features and use the Anchor Graph method to construct a graph. This process is repeated many times to obtain multiple graphs, a process which can be implemented in parallel to ensure runtime efficiency. Then the multiple graphs vote to determine the labels for the unlabeled data. We argue that the randomness can be viewed as a kind of regularization. We evaluate the proposed method on eight real-world data sets by comparing it with two traditional graph-based methods and one state-of-the-art semi-supervised learning method based on Anchor Graph to show its effectiveness. We also apply the proposed method to the subject of face recognition. |
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