A General Model for Semi-Supervised Dimensionality Reduction
Xuesong Yin, Ting Shu, Qi Huang · Procedia Engineering · 2012
This paper focuses on semi-supervised dimensionality reduction. In this scenario, we present a general model for semi-supervised dimensionality reduction with pairwise constraints (SSPC). Through defining a discriminant adjacent matrix, SSPC learns a projection embedding the data from the original space to the low-dimensional space such that intra-cluster instances become even more nearby while extra-cluster instances become as far away from each other as possible. Experimental results on a collection of benchmark data sets show that SSPC is superior to many established dimensionality reduction methods.