Regularization theory and neural networks architectures
Neural Computation
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
On different facets of regularization theory
Neural Computation
Assessment of time dependency in face recognition: an initial study
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Partial relevance in interactive facial image retrieval
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
Discriminative components of data
IEEE Transactions on Neural Networks
Face recognition using parzenfaces
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Learning neighborhoods for metric learning
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
| Hi-index | 0.00 |
Discriminative feature extraction is one of the fundamental problems in pattern recognition and signal processing. It was recently proposed that maximizing the class prediction by neighboring samples in the transformed space is an effective objective for learning a low-dimensional linear embedding of labeled data. The associated methods, Neighborhood Component Analysis (NCA) and Relevant Component Analysis (RCA), have been proven to be useful preprocessing techniques for discriminative information visualization and classification. We point out here that NCA and RCA are prone to overfitting and therefore regularization is required. NCA and RCA's failure for high-dimensional data is demonstrated in this paper by experiments in facial image processing. We also propose to incorporate a Gaussian prior into the NCA objective and obtain the Regularized Neighborhood Component Analysis (RNCA). The empirical results show that the generalization can be significantly enhanced by using the proposed regularization method.