Title :
Out-of-Sample Embedding for Manifold Learning Applied to Face Recognition
Author :
Dornaika, Fadi ; Raduncanu, B.
Author_Institution :
Univ. of the Basque Country UPV/EHU, San Sebastian, Spain
Abstract :
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.
Keywords :
eigenvalues and eigenfunctions; face recognition; graph theory; image classification; image coding; learning (artificial intelligence); support vector machines; KNN classifier; KSVM classifier; Laplacian eigenmaps; adjacency graphs; automatic graph reconstruction; face recognition problem; k-nearest neighbor classifier; kernel support vector machines classifier; manifold learning techniques; nonlinear embeddings; nonlinear manifolds; out-of-sample embedding; public face databases; sparse coding theory; test data; Face; Face recognition; Kernel; Laplace equations; Manifolds; Support vector machines; Training;
Conference_Titel :
Computer Vision and Pattern Recognition Workshops (CVPRW), 2013 IEEE Conference on
Conference_Location :
Portland, OR
DOI :
10.1109/CVPRW.2013.127