TY  - CONF
AU  - Fadi Dornaika
AU  - Bogdan Raducanu
A2  - CVPRW
PY  - 2013//
TI  - Out-of-Sample Embedding for Manifold Learning Applied to Face Recognition
BT  - IEEE International Workshop on Analysis and Modeling of Faces and Gestures
SP  - 862
EP  - 868
N2  - Manifold learning techniques are affected by two critical aspects: (i) the design of the adjacency graphs, and (ii) the embedding of new test data---the out-of-sample problem. For the first aspect, the proposed schemes were heuristically driven. For the second aspect, the difficulty resides in finding an accurate mapping that transfers unseen data samples into an existing manifold. Past works addressing these two aspects were heavily parametric in the sense that the optimal performance is only reached for a suitable parameter choice that should be known in advance. In this paper, we demonstrate that sparse coding theory not only serves for automatic graph reconstruction as shown in recent works, but also represents an accurate alternative for out-of-sample embedding. Considering for a case study the Laplacian Eigenmaps, we applied our method to the face recognition problem. To evaluate the effectiveness of the proposed out-of-sample embedding, experiments are conducted using the k-nearest neighbor (KNN) and Kernel Support Vector Machines (KSVM) classifiers on four public face databases. The experimental results show that the proposed model is able to achieve high categorization effectiveness as well as high consistency with non-linear embeddings/manifolds obtained in batch modes.
L1  - http://refbase.cvc.uab.es/files/DoR2013.pdf
UR  - http://dx.doi.org/10.1109/CVPRW.2013.127
N1  - OR; 600.046;MV
ID  - Fadi Dornaika2013
ER  -