NDLI: Semi-supervised local-learning-based feature selection

Content Provider	IEEE Xplore Digital Library
Author	Wang, J.J.-Y. Jin Yao Yijun Sun
Copyright Year	2014
Description	Author affiliation: SUNY - Univ. at Buffalo, Buffalo, NY, USA (Wang, J.J.-Y.; Jin Yao; Yijun Sun)
Abstract	Local-learning-based feature selection has been successfully applied to high-dimensional data analysis. It utilizes class labels to define a margin for each data sample and selects the most discriminative features by maximizing the margins with regard to a feature weight vector. However, it requires that all data samples are labeled, which makes it unsuitable for semi-supervised learning where only a handful of training samples are labeled while most are unlabeled. To address this issue, we herein propose a new semi-supervised local-learning-based feature selection method. The basic idea is to learn the class labels of unlabeled samples in a new feature subspace induced by the learned feature weights, and then use the learned class labels to define the margins for feature weight learning. By constructing and optimizing a unified objective function, the feature weights and class labels are learned simultaneously in an iterative algorithm. The experiments performed on some benchmark data sets show the advantage of the proposed algorithm over stat-of-the-art semi-supervised feature selection methods.
Starting Page	1942
Ending Page	1948
File Size	1123183
Page Count	7
File Format	PDF
ISSN	21614407
e-ISBN	9781479914845
DOI	10.1109/IJCNN.2014.6889591
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vectors Face Educational institutions Training Manifolds Optimization Image reconstruction
Content Type	Text
Resource Type	Article

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