In the last decade, kernel-based learning has become a state-of-the-art technology in Machine Learning. We briefly review kernel PCAKernel principal component analysis (kPCA) (kPCA) and the pre-image problem that occurs in kPCA. Subsequently, we discuss a novel direction where kernel-based models are used for property optimization. For this purpose, a stable estimation of the model’s gradient is essential and non-trivial to achieve. The appropriate use of pre-image projections is key to successful gradient-based optimization—as will be shown for toy and real-world problems from quantum chemistry and physics.
|Title of host publication||Empirical Inference: Festschrift in Honor of Vladimir N. Vapnik|
|Publisher||Springer Berlin Heidelberg|
|Number of pages||15|
|ISBN (Print)||9783642411366, 9783642411359|
|Publication status||Published - 2013 Jan 1|
ASJC Scopus subject areas
- Computer Science(all)