NDLI: Graph-based semi-supervised multi-label learning method

Content Provider	IEEE Xplore Digital Library
Author	Zhang Chen-Guang Zhang Xia-Huan
Copyright Year	2013
Description	Author affiliation: Coll. of Inf. & Technol., Hainan Univ., Haikou, China (Zhang Chen-Guang; Zhang Xia-Huan)
Abstract	The problem of multi-label classification has attracted great interest in the last decade. However, most multi-label learning methods only focus on supervised settings, and can not effectively make use of relatively inexpensive and easily obtained large number of unlabeled samples. To solve this problem, we put forward a novel graph-based semi-supervised multi-label learning method, called GSMM. GSMM characterize the inherent correlations among multiple labels by Hilbert-Schmidt independence criterion. It's expected to derive the optimal assignment of class membership to unlabeled samples by maximizing the consistency of class label correlations and simultaneously as smooth as possible on sample feature graph. The experiments comparing GSMM to the state-of-the-art multi-label learning approaches on several real-world datasets show GSMM can effectively learn from the labeled and unlabeled samples. Especially when the labeled is relatively rare, it can improve the performance greatly.
Starting Page	1021
Ending Page	1025
File Size	136376
Page Count	5
File Format	PDF
ISBN	9781479925643
e-ISBN	9781479925650
DOI	10.1109/MEC.2013.6885211
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement Learning systems Correlation Graph based semi-superivsed learning Hilbert-Schimidt independence criterion Multi-label learning Vectors Classification algorithms Kernel Optimization
Content Type	Text
Resource Type	Article

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