Authors:
Thanh-Binh Le
and
Sang-Woon Kim
Affiliation:
Myongji University, Korea, Republic of
Keyword(s):
Semi-supervised Learning, Selecting Unlabeled Data, Multi-view Learning Techniques.
Related
Ontology
Subjects/Areas/Topics:
Classification
;
Pattern Recognition
;
Theory and Methods
Abstract:
In a semi-supervised learning approach, using a selection strategy, strongly discriminative examples are first
selected from unlabeled data and then, together with labeled data, utilized for training a (supervised) classifier.
This paper investigates a new selection strategy for the case when the data are composed of different multiple
views: first, multiple views of the data are derived independently; second, each of the views are used for measuring
corresponding confidences with which examples to be selected are evaluated; third, all the confidence
levels measured from the multiple views are used as a weighted average for deriving a target confidence; this
selecting-and-training is repeated for a predefined number of iterations. The experimental results, obtained
using synthetic and real-life benchmark data, demonstrate that the proposed mechanism can compensate for
the shortcomings of the traditional strategies. In particular, the results demonstrate that when the data is appropri
ately
decomposed into multiple views, the strategy can achieve further improved results in terms of the
classification accuracy.
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