On Selecting Useful Unlabeled Data Using Multi-view Learning Techniques
Thanh-Binh Le, Sang‐Woon Kim · 2015
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 appropriately decomposed into multiple views, the strategy can achieve further improved results in terms of the classification accuracy.