Notice of RetractionSemi-Supervised short text categorization based on Random Subspace

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

In order to solve the “labeling bottleneck” problem of short text categorization, a novel Semi-Supervised Expectation-Maximization short text categorization method based on Random Subspace (RS-EM) is used in this paper. RS-EM performs an iterative EM style training where multiple models are trained on subsets by using random subspace method. This combination of the stochastic discrimination theory and semi-supervised EM algorithm is used to compensate for the weaknesses of the standard EM algorithm to over-training. Experimental on real corpus show that the proposed method is more effectively exploit unlabeled data to enhance the learning performance, and is superior to standard semi-supervised EM algorithm in the learning efficiency and the classification generalization.