NDLI: Active learning of Gaussian processes with manifold-preserving graph reduction

Content Provider	SpringerLink
Author	Zhou, Jin Sun, Shiliang
Copyright Year	2014
Abstract	As a recently proposed machine learning method, active learning of Gaussian processes can effectively use a small number of labeled examples to train a classifier, which in turn is used to select the most informative examples from unlabeled data for manual labeling. However, in the process of example selection, active learning usually need consider all the unlabeled data without exploiting the structural space connectivity among them. This will decrease the classification accuracy to some extent since the selected points may not be the most informative. To overcome this shortcoming, in this paper, we present a method which applies the manifold-preserving graph reduction (MPGR) algorithm to the traditional active learning method of Gaussian processes. MPGR is a simple and efficient example sparsification algorithm which can construct a subset to represent the global structure and simultaneously eliminate the influence of noisy points and outliers. Thereby, when actively selecting examples to label, we just choose from the subset constructed by MPGR instead of the whole unlabeled data. We report experimental results on multiple data sets which demonstrate that our method obtains better classification performance compared with the original active learning method of Gaussian processes.
Starting Page	1615
Ending Page	1625
Page Count	11
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	25
Issue Number	7-8
e-ISSN	14333058
Language	English
Publisher	Springer London
Publisher Date	2014-06-06
Publisher Place	London
Access Restriction	Subscribed
Subject Keyword	Manifold-preserving graph reduction Space connectivity Active learning Gaussian process Artificial Intelligence (incl. Robotics) Data Mining and Knowledge Discovery Probability and Statistics in Computer Science Computational Science and Engineering Image Processing and Computer Vision Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Software Artificial Intelligence

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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