NDLI: A Semi-Supervised Relief Based Feature Extraction Algorithm

Content Provider	IEEE Xplore Digital Library
Author	Xiaoming Liu Jinshan Tang Jun Liu Zhilin Feng
Copyright Year	2008
Description	Author affiliation: Coll. of Comput. Sci. & Technol., Wuhan Univ. of Sci. & Technol., Wuhan (Xiaoming Liu; Jinshan Tang; Jun Liu) \|\| Zhijiang Coll., Zhejiang Univ. of Technol., Hangzhou (Zhilin Feng)
Abstract	Local Feature Extraction (LFE) algorithm is an effective feature extraction method developed in recent years. One of the shortcomings of the current LFE algorithm is that it can only process labeled data, and does not work well when the amount of the labeled data is limited. However, it is usually easy to obtain large amount of unlabeled data but only a few labeled data. In this paper, we propose a new feature extraction algorithm, called Semi-Supervised LFE (SSLFE), which can handle both labeled and unlabeled data to perform feature extraction. In the proposed algorithm, the labeled data are used to maximize the margin and the unlabeled data are used as regulations with respect to the intrinsic geometric structure of the data. The final projection matrix can be obtained by eigenvalue decomposition. Experiments on several datasets demonstrate that SSLFE achieves much higher classification accuracy than LFE.
Starting Page	3
Ending Page	6
File Size	248077
Page Count	4
File Format	PDF
ISBN	9781424434305
DOI	10.1109/FGCNS.2008.39
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer science Conferences Supervised learning Humans Semisupervised learning Feature extraction Educational institutions Eigenvalues and eigenfunctions Linear discriminant analysis Principal component analysis
Content Type	Text
Resource Type	Article

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