Neighbourhood preserving based semi-supervised dimensionality reduction

A semi-supervised linear dimensionality reduction method based on side information and neighbourhood preserving is proposed. In this problem, only must-link constraints (pairs of instances belong to the same class) and cannot-link constraints (pairs of instances belong to different classes) are given. The proposed neighbourhood preserving based semi-supervised dimensionality reduction algorithm can not only preserve the must-link and cannot-link constraints but can preserve the local structure of the input data in the low dimensional embedding subspace. Experimental results on several datasets demonstrate the effectiveness of the method.