Kernel Principal Component Analysis
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
A tutorial on support vector regression
Statistics and Computing
2005 Speical Issue: Graph kernels for chemical informatics
Neural Networks - Special issue on neural networks and kernel methods for structured domains
Efficient Computation of Recursive Principal Component Analysis for Structured Input
ECML '07 Proceedings of the 18th European conference on Machine Learning
Recursive principal component analysis of graphs
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Exact solutions for recursive principal components analysis of sequences and trees
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
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In recent years, more and more attention has been paid on learning in structured domains, e.g. Chemistry. Both Neural Networks and Kernel Methods for structured data have been proposed. Here, we show that a recently developed technique for structured domains, i.e. PCA for structures, permits to generate representations of graphs (specifically, molecular graphs) which are quite effective when used for prediction tasks (QSAR studies). The advantage of these representations is that they can be generated automatically and exploited by any traditional predictor (e.g., Support Vector Regression with linear kernel).