Online feature extraction based on accelerated kernel principal component analysis for data stream - Annie, Joseph; Takaomi, Tokumoto; Seiichi, Ozawa (Springer Berlin Heidelberg, 2016)
Kernel principal component analysis (KPCA) is known as a nonlinear feature extraction method. Takeuchi et al. have proposed an incremental type of KPCA (IKPCA) that can update an eigen-space incrementally for a sequence of data. However, in IKPCA, the eigenvalue decomposition should be carried out for every single data, even though a chunk of data is given at one time. To reduce the computational costs in learning chunk data, this paper proposes an extended IKPCA called Chunk IKPCA (CIKPCA) where a chunk of multiple data is learned with single eigenvalue decomposition. For a large data chunk, to reduce further computation time and memory usage, it is first divided into several smaller chunks, and only useful data are selected based on the accumulation ratio. In the proposed CIKPCA, a small set of independent data are first selected from a reduced set of data so that eigenvectors in a high-dimensional feature space can be represented as a linear combination of such independent data. Then, the eigenvectors are incrementally updated by keeping only an eigenspace model that consists of the sextuplet such as independent data, coefficients, eigenvalues, and mean information. The proposed CIKPCA can augment an eigen-feature space based on the accumulation ratio that can also be updated without keeping all the past data, and the eigen-feature space is rotated by solving an eigenvalue problem once for each data chunk. The experiment results show that the learning time of the proposed CIKPCA is greatly reduced as compared with KPCA and IKPCA without sacrificing recognition accuracy.