Abstract
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).
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Cardin, R., Michielan, L., Moro, S., Sperduti, A. (2009). PCA-Based Representations of Graphs for Prediction in QSAR Studies. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5769. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04277-5_11
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DOI: https://doi.org/10.1007/978-3-642-04277-5_11
Publisher Name: Springer, Berlin, Heidelberg
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