Orthogonal Neighborhood Preserving Projection using L1-norm Minimization

Subspace analysis or dimensionality reduction techniques are becoming very popular for many computer vision tasks including face recognition or in general image recognition. Most of such techniques deal with optimizing a cost function using L2-norm. However, recently, due to capability of handling outliers, optimizing such cost function using L1-norm is drawing the attention of researchers. Present work is the first attempt towards the same goal where Orthogonal Neighbourhood Preserving Projection (ONPP) technique is optimized using L1-norm. In particular the relation of ONPP and PCA is established in the light of L2-norm and then ONPP is optimized using an already proposed mechanism of L1-PCA. Extensive experiments are performed on synthetic as well as real data. It has been observed that L1-ONPP outperforms its counterpart L2-ONPP.