Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fast incremental kernel principal component analysis for online feature extraction
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Representing image matrices: eigenimages versus eigenvectors
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Recognizing face or object from a single image: linear vs. kernel methods on 2d patterns
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Incremental Learning of Chunk Data for Online Pattern Classification Systems
IEEE Transactions on Neural Networks
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In this paper, we propose a new online non-linear feature extraction method, called the incremental two-dimensional kernel principal component analysis (I2DKPCA), not only to reduce the computational cost but also to provide good feature representation. Batch type feature extraction methods such as principal component analysis (PCA) and two-dimensional PCA (2DPCA) require more computational time and memory usage, as they collect the entire training data to extract the basis vectors. Also, these linear feature extraction methods could not effectively represent the non-linear distribution of input data. Therefore, by adopting a non-linear kernel approach with chunk concept, the KPCA and 2DKPCA can effectively address the non-linear feature representation problem by adaptively changing the feature spaces. However, this kernel approach requires more computational time for processing images with high dimensional input data. In order to solve these problems, we combined the 2DKPCA with incremental learning for (1) solving the non-linear problem and (2) reducing the memory usage with computational time. In order to evaluate the performance of I2DKPCA, several experiments have been performed using well-known face and object image databases.