A property of learning chunk data using incremental kernel principal component analysis
Takaomi Tokumoto, Seiichi Ozawa · 2012
An incremental learning algorithm of Kernel Principal Component Analysis (KPCA) called Chunk Incremental KPCA (CIKPCA) has been proposed for online feature extraction in pattern recognition. CIKPCA can reduce the number of times to solve the eigenvalue problem compared with the conventional incremental KPCA when a small number of data are simultaneously given as a stream of data chunks. However, our previous work suggests that the computational costs of the independent data selection in CIKPCA could dominate over those of the eigenvalue decomposition when a large chunk of data are given. To verify this, we investigate the influence of the chunk size to the learning time in CIKPCA. As a result, CIKPCA requires more learning time than IKPCA unless a large chunk of data are divided into small chunks (e.g., less than 50).