NDLI: Transductive Ordinal Regression

Content Provider	IEEE Xplore Digital Library
Author	Chun-Wei Seah Tsang, I.W. Yew-Soon Ong
Copyright Year	2012
Abstract	Ordinal regression is commonly formulated as a multiclass problem with ordinal constraints. The challenge of designing accurate classifiers for ordinal regression generally increases with the number of classes involved, due to the large number of labeled patterns that are needed. The availability of ordinal class labels, however, is often costly to calibrate or difficult to obtain. Unlabeled patterns, on the other hand, often exist in much greater abundance and are freely available. To take benefits from the abundance of unlabeled patterns, we present a novel transductive learning paradigm for ordinal regression in this paper, namely transductive ordinal regression (TOR). The key challenge of this paper lies in the precise estimation of both the ordinal class label of the unlabeled data and the decision functions of the ordinal classes, simultaneously. The core elements of the proposed TOR include an objective function that caters to several commonly used loss functions casted in transductive settings, for general ordinal regression. A label swapping scheme that facilitates a strictly monotonic decrease in the objective function value is also introduced. Extensive numerical studies on commonly used benchmark datasets including the real-world sentiment prediction problem are then presented to showcase the characteristics and efficacies of the proposed TOR. Further, comparisons to recent state-of-the-art ordinal regression methods demonstrate the introduced transductive learning paradigm for ordinal regression led to the robust and improved performance.
Page Count	13
File Size	2275471
Starting Page	1074
Ending Page	1086
File Format	PDF
ISSN	2162237X
Volume Number	23
Issue Number	7
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Multiprotocol label switching Training Motion pictures Vectors Fasteners Prediction algorithms Convergence transductive learning Cluster assumption ordinal classification ordinal loss function ordinal regression (OR) support vector machines (SVMs)
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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