Discriminative semi-supervised dimensionality reduction with pairwise constraints for face clustering
Cui Ying-ji · Heilongjiang Science · 2014
With high-dimensional unstructured data available in great numbers,dimensionality reduction is more and more important in data processing. In this paper,an instance-level based discriminative semi-supervised dimensionality reduction method with chunklets named IDSDRC is also proposed, which aims to simultaneously use both instance-level and chunklet-level information together with unlabeled data for dimensionality reduction. Experimental results on standard face databases for face clustering show that IDSDRC is efficient and superior to several established semi-supervised dimensionality reduction methods.