Label propagation on data with multiple representations through multi-graph locality preserving projections
Olga Zoidi, Nikos Nikolaidis, Ioannis Pitas · 2014
In this paper a novel method is introduced for propagating label information on data with multiple representations. The method performs dimensionality reduction of the data by calculating 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. The final data representations are then fused, in order to perform label propagation. The performance of the proposed method was evaluated on facial images extracted from stereo movies and on the UCF11 action recognition database. Experimental results showed that the proposed method outperforms state of the art methods.