Incremental two-dimensional kernel principal component analysis

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.