NDLI: Adaptive Constraint Propagation for Semi-Supervised Kernel Matrix Learning

Content Provider	Springer Nature Link
Author	Jian, Meng Jung, Cheolkon Shen, Yanbo Jiao, Licheng Liu, Juan
Copyright Year	2014
Abstract	We propose adaptive constraint propagation (ACP) for semi-supervised kernel matrix learning (SS-KML). SS-KML aims to learn a kernel matrix from the given samples which contains just a little supervised information such as class labels or pairwise constraints. Recently, for effective SS-KML, constraint propagation methods by semi-definite programming are being actively studied, and the representative works are pairwise constraint propagation (PCP) and kernel propagation (KP). They have used hard constraints in their frameworks for constraint propagation and achieved outstanding classification performance. However, a small set of the hard constraints sometimes cannot cover all the samples of the full data set, and thus lead to large distortions of global discriminative data structure in the learned kernel matrix. It has a negative influence on the classification performance. To deal with this problem, we provide two adaptive fidelity terms to satisfy the requirement that two must-link samples become close and two cannot-link samples become far apart. Then, we build a new framework based on them to adaptively propagate the constraints. Experimental results demonstrate that ACP outperforms state-of-the-art SS-KML methods such as PCP and KP in terms of both effectiveness and efficiency.
Starting Page	107
Ending Page	123
Page Count	17
File Format	PDF
ISSN	13704621
Journal	Neural Processing Letters
Volume Number	41
Issue Number	1
e-ISSN	1573773X
Language	English
Publisher	Springer US
Publisher Date	2014-01-12
Publisher Place	Boston
Access Restriction	Subscribed
Subject Keyword	Adaptive constraint propagation Pairwise constraint Semi-definite programming Semi-supervised kernel matrix learning Semi-supervised classification Artificial Intelligence (incl. Robotics) Statistical Physics, Dynamical Systems and Complexity Computational Intelligence
Content Type	Text
Resource Type	Article
Subject	Neuroscience Artificial Intelligence Computer Networks and Communications Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
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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