Abstract
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.
Original language | English |
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Title of host publication | Empirical Inference |
Subtitle of host publication | Festschrift in Honor of Vladimir N. Vapnik |
Publisher | Springer Berlin Heidelberg |
Pages | 245-259 |
Number of pages | 15 |
ISBN (Electronic) | 9783642411366 |
ISBN (Print) | 9783642411359 |
DOIs | |
Publication status | Published - 2013 Jan 1 |
Bibliographical note
Publisher Copyright:© Springer-Verlag Berlin Heidelberg 2013.
ASJC Scopus subject areas
- General Computer Science