Semi-supervised dimensionality reduction on data with multiple representations for label propagation on facial images
Olga Zoidi, Nikos Nikolaidis, Ioannis Pitas · 2014
In this paper a novel method is introduced for semi-supervised dimensionality reduction on facial images extracted from stereo videos. It operates on image data with multiple representations and calculates a projection matrix that preserves locality information and a priori pairwise information, in the form of must-link and cannot-link constraints between the various data representations, as well as label information for a percentage of the data. The final data representation is a linear combination of the projections of all data representations. The performance of the proposed Semi-supervised Multiple Locality Preserving Projections method was evaluated in person identity label propagation on facial images extracted from stereo movies. Experimental results showed that the proposed method outperforms state of the art methods.