Incremental Kernel PCA for Efficient Non-linear Feature Extraction
Tiffany Chin, David Suter · 2006
The Kernel Principal Component Analysis (KPCA) has been effectively applied as an unsupervised non-linear feature extractor in many machine learning applications. However, with a time complexity of O(n3), the practicality of KPCA on large datasets is minimal. In this paper, we propose an approximate incremental KPCA algorithm which allows efficient processing of large datasets. We extend a linear PCA updating algorithm to the non-linear case by utilizing the kernel trick, and apply a reduced set construction method to compress expressions for the derived KPCA basis at each update. In addition, we show how multiple feature space vectors can be compressed efficiently, and how approximated KPCA bases can be re-orthogonalized using the kernel trick. The proposed method is justified through experimental validations.